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Research Article
Visualization of Information Retrieval in Smart Library Based on
Virtual Reality Technology
Shulin Fang
Xi’an Academy of Fine Arts, Xi’an, Shaanxi 710065, China
Correspondence should be addressed to Shulin Fang; 50041@xafa.edu.cn
Received 23 October 2020; Revised 16 November 2020; Accepted 18 November 2020; Published 29 November 2020
Academic Editor: Zhihan Lv
Copyright ©2020 Shulin Fang. is is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Starting from the virtual reality technology, the characteristics of its most suitable combination with the library are explored, so as
to lay a foundation in theory and practice to promote the development of virtual reality in the library. In the concentration camp of
the latest advanced technology, the relevant technologies used in the various levels of models in the smart library are extracted, and
their functional principles and applications are systematically introduced; Chapter 4 builds the level of the smart library in-
formation retrieval technology model. e structure diagram, various levels of functions, and related smart service models are
discussed. Research is on context construction of extended resources of knowledge service in smart libraries. e elements and
content of the resource context are introduced, and the strategy of constructing the resource context for the extension of the
knowledge service of the smart library is proposed. Research is on the construction of the context of the extension and in-
terconnection of knowledge services in smart libraries. e relevant elements and contents of the technical context and the spatial
context are introduced, and a strategy for constructing a connected context for the extension of the knowledge service of a smart
library is proposed. Research is on the context construction of knowledge service extension of smart library. e elements and
contents of the service context are introduced, and the strategy of constructing the service context of the knowledge service
extension of the smart library is proposed. A visual model of information retrieval is constructed. e model integrates the core
steps of the information visualization process and introduces the information space and functional system (navigation, orga-
nization, indexing, and retrieval) in the information construction theory into it and through five mapping layers (functional space
mapping, visualization space mapping, visualization mapping, view mapping, and interactive mapping) and six spatial layers (role
space, information space, functional space, visualized information space, visualized object space, and visualized view space), which
describe visualization applications that target the user experience model in information construction build process.
1. Introduction
With the advent of the big data era, a large amount of data
continues to emerge, and there is a phenomenon of
abundant information but there is a lack of useful infor-
mation. Information overload has caused problems such as
lack of information and a significant reduction in user in-
formation utilization. As an indispensable part of society,
libraries should bring forth the new and avoid working
behind closed doors. ey need to adapt to the development
of the times and integrate the latest technology into the
library to achieve personalized services for users. But no
matter how the library changes and how the technology is
updated alternately, its core value of “people-oriented” has
not changed. How can the library realize the transformation
from the passive and stereotyped model to the intelligent
service model in the new intelligent environment and realize
the transformation from single-library service to cross-li-
brary service? e construction of the technical model of
China’s smart library studied in this paper provides technical
support for the above problems. Relying on the construction
of this model, the library can realize transparent manage-
ment of user services, mine users’ useful information needs,
and intelligently predict user personalized services trend,
accurately integrating the value density of library manage-
ment, scientific services, and librarian decision-making.
Research on smart libraries can suggest that it is a dif-
ficult and tedious task for readers to accurately find the
Hindawi
Complexity
Volume 2020, Article ID 6646673, 18 pages
https://doi.org/10.1155/2020/6646673
books and materials they need in the library environment,
but location-aware technology can help readers solve such
problems and achieve the purpose of accurate search [1, 2]; it
is proposed that a smart library is a mobile library that can
transcend space limitations and can be noticed by people
[3, 4]. e fact that a smart library uses a large number of
software quality projects to reduce users and libraries is
emphasized. e goal of error is in the process of use,
classification, configuration, installation, or processing by
technical personnel [5]. Research is on the application of
data mining technology in smart libraries. rough this
interactive platform system, the library uses EAI technology
to construct the underlying architecture, exchange hetero-
geneous systems, data, and platforms, realize seamless in-
tegration within the system and between multiple systems,
and then uses scene graph, data mining, and data analysis
technology which intelligently perceives, mines, captures,
analyzes, and integrates information to achieve collabora-
tion and sharing of data processing [6, 7]. In the era of big
data, data mining technology is of great significance to the
development of libraries. It not only can extract hidden and
potentially valuable knowledge information from a large
amount of disordered and vague practical application data
but also can be used to support a wide range of business
smart applications, such as directional marketing [8, 9].
Smart library mobile visual search service architecture di-
agram, and display mobile visual search services, visuali-
zation services, knowledge services, social services, social
recommendations, one-stop navigation services, and other
service functions in the smart service module [10, 11]. In-
novative service models have distinctive features, and these
accompanying features have evolved based on new tech-
nologies and intelligent facilities. Research is on location-
based information push services and private customization
and other service models, and the research and innovative
development of smart libraries by expanding social networks
and expanding publicity camps have increased [12]. Guided
by relevance learning theory, service models of context-
aware services, social services, cloud services, and mobile
information services are constructed [13]. e deep inte-
gration of “Internet +” and the library has realized the in-
terconnection between the physical world and the virtual
world, making the library rich and diverse, and the library
community is also actively studying this direction [14].
Under the background of the deep integration of traditional
industries and the Internet, it is proposed that libraries
should establish user thinking and Internet thinking to build
a smart library. Internet thinking is the technical basis of
user thinking, and user thinking is the manifestation of
Internet thinking, include two complement segmentation
[15, 16]. In the “Internet +” environment, the service content
of the smart library is introduced in detail [17]. Information
technology plays an important role in the transformation
and upgrading of the library. e use of Internet of ings,
cloud computing, wearable technology, virtual reality
technology, artificial intelligence, and other technologies can
realize the optimization of the library’s literature, equip-
ment, personnel, and buildings [18, 19]. Based on the
problems in the application of the Internet of ings
identification technology in the smart library, the im-
provement measures for the construction of the smart li-
brary are proposed [20, 21]. e three relationships between
wearable technology and smart library, the specific appli-
cation of wearable technology in reader navigation, and help
for disadvantaged groups and personalized services are
analyzed [22, 23]. Using the effect of virtual reality to present
pictures, models, or videos in the book, the reading method
and interest are increased [24, 25]. A three-dimensional
book of virtual reality is made, and the pictures in the book
are carried out by using mobile devices. e scanning
function can present the actual scene of the corresponding
3D model, change the interactive way of parent-child
reading, and promote the establishment of the reading
sharing relationship between parent and child [26]. Mobile
learning is most effective only when it connects the real
environment with related resources [27, 28]. Mobile AR
technology provides a channel for the establishment of this
connection, and library resources make the connection
possible. In the research of the library personalized service
system based on virtual reality, there is a detailed intro-
duction to the realization of personalized service related
functions, including the following: real-time scanning of QR
codes, real-time calculation of projection matrix, projection
of three-dimensional objects and location-based book re-
trieval and book recommendation functions of [29, 30]. In
the research on the application of virtual reality technology
in the library personalized service platform, it is found that
the library information browsing system based on QR code
and virtual reality can present different information inter-
faces to different users according to the information dis-
played by scanning the QR code. It guides readers to browse
the information of books on the bookshelf in an intuitive
way and at the same time recommends various resources of
interest to readers [31]. e Android-based virtual reality
library service platform is a platform that combines new
technologies such as the mobile Internet and data mining
technology to provide users with personalized services such
as book query, book recommendation, and book location
navigation [32]. e impact of various frontier technology
developments is on libraries, including virtual reality
technology. e ideas and countermeasures to innovate the
library service model for reference from the Internet are put
forward [33]. In the exploration of the application of virtual
reality technology in the library, starting from the practical
application direction of the library, the foundation of virtual
reality is introduced in detail, the important role of this
technology in the library business is explored, and the effect
of virtual reality in the library is initially explored [34]. e
application status of mobile virtual reality technology in the
library, combined with the characteristics of various services
of the library, is analyzed and the application prospects of
mobile virtual reality technology in the library are also
explored in order to further utilize the mobile virtual reality
technology in the library. e application value in the library
provides reference [35]. From the perspective of bookshelf
and resource integration, virtual reality multimedia books,
virtual reality library navigation, virtual reality optical
character recognition, and virtual reality personalized
2Complexity
services, the application of mobile virtual reality technology
in modern libraries is discussed [36, 37]. rough research
and analysis, the necessity of using virtual reality technology
in smart libraries is analyzed, and the application form,
workflow, and image recognition mechanism of virtual
reality in smart libraries are pointed out, and the innovative
service mode of smart libraries based on virtual reality
technology is explored [38]. e current situation of library
business at this stage is analyzed, and, according to the
characteristics of virtual reality, the application of virtual
reality technology in intelligent libraries from three aspects
is discussed: library personalized intelligent navigation
service, resource integration service, and personalized rec-
ommendation push service [39, 40].
In view of the research on the information retrieval
technology model of the smart library, a detailed analysis is
made on the specific construction content of the smart
perception layer, network transmission layer, data resource
layer, and smart application layer. is article mainly focuses
on personalized and scenario-based recommendation, and
virtual research is carried out on six service dimensions of
reality, multimedia, smart space, and visualization. From a
new perspective, virtual reality technology has been widely
popularized in certain fields and has brought about certain
benefits to those fields. One of the evaluation criteria for the
effect of the integration of new technology is whether it has
improved the industry, and the application of virtual reality
in the library is consolidated through various practical
improvement effects, so as to lay the foundation for the
popularization of the library industry, as well as theory and
practice to be prepared for in-depth research. It also pro-
poses a strategy for constructing a resource context for the
extension of knowledge services in a smart library to in-
troduce the elements and content of a resource context, a
strategy for constructing a context for the extension of
knowledge services in a smart library, and a strategy for
constructing a context for extension services of a smart li-
brary. e application of augmented reality, virtual reality,
and integrated reality provides a new way for libraries to
carry out innovative services and a new perspective for li-
braries to better serve readers. With the passage of time, the
technologies of virtual reality, augmented reality, and in-
tegrated reality will continue to develop, and the applications
full of surprises will continue to change the way readers
work, communicate, and entertain themselves and further
expand the functions of libraries, so as to promote the
construction and development of smart libraries.
2. Information Retrieval Model of Smart Library
Based on Virtual Reality
With the current rapid development of intelligence and
Internet of ings technology, a technical platform has been
created for the bold use of virtual reality technology in li-
braries, and it has also provided technical innovation and
service concept transformation for the diversified trans-
formation of library services. Virtual reality technology is to
reflect virtual information into the real world with the help
of computer processing technology to realize the integration
of virtual objects, scenes, actions, and other objects and
apply them to real scenes. e introduction of virtual reality
technology in the smart library creates a comfortable smart
virtual space for readers, allowing readers to enhance their
desire to enjoy library services in the context of services.
Virtual reality technology is a special form of reality tech-
nology. It has the characteristics of strong interaction, in-
tegration of virtuality and reality, and three-dimensional
positioning. It introduces three-dimensional registration
and virtual compatibility. At present, if libraries want to
efficiently broaden the scope of public cultural services, they
should introduce AR virtual reality technology as soon as
possible, show the service model to readers in a brand new
form, and use high-quality resource construction to burst
out the library’s own advantages.
2.1. Construction of the Technical Hierarchical Model of Smart
Library. e library uses its own obvious resource advan-
tages and integrates the advanced service performance of
smart libraries, combines the library models built by pre-
decessors, and introduces smart modern technology based
on the theory of integration and collaboration to build a
technical-level technical-service model that meets user
needs. As shown in Figure 1, the current situation that li-
braries use a general service mode to meet user needs is
broken, and the stable development of the library’s service
concept of “serving users and satisfying users” is promoted.
e architecture shown in Figure 1 is mainly composed
of four parts: smart perception layer, network transmission
layer, data resource layer, and smart application layer. e
smart perception layer in the smart library is mainly
composed of wearable devices, sensors, storage devices,
RFID, and video. It consists of monitoring equipment and
network monitoring equipment. Users visit the library under
the information demand target. e intelligent equipment in
the intelligent perception layer perceives, screens, and ex-
tracts the data traces generated during the reader’s enjoy-
ment of the library’s application mode and services. rough
the network transmission layer, according to the distance
between the geographic location of the sensing device and
the library collection data, the large amount of data collected
by the intelligent sensing layer can be safely, efficiently, and
quickly transmitted through the wireless network, triple play
technology, and computer communication network.
e data resource layer is at the center of the technical
model. It consists of data warehousing, data mining, cloud
computing, information push, and semantic analysis tech-
nologies. It is mainly responsible for user data storage and
format conversion, user data resource mining and calcu-
lation, and user personalized information needs. Task
functions include prediction, recommendation, and man-
agement. e smart application layer relies on the data
analysis provided by the data resource layer and is mainly
constructed by virtual reality technology, multimedia, data
visualization, and other technologies to realize library scene-
based recommendation services, user personalized services,
virtual reality services, and multimedia services. e im-
provement of smart space services and visualization services
Complexity 3
and the innovative development of smart libraries have
realized the service process from sensing information to
digging information, processing information, and finally
discovering wisdom.
Virtual reality technology has computer-generated
three-dimensional effects that integrate visual, tactile, and
olfactory functions, allowing users to enjoy interactive
services immersively with realistic and visual scenes. e
multiple senses, visibility, permeability, and immersion
characteristics of virtual reality technology make it popular
in the library field. At present, virtual reality technology is
mainly used in the virtual official buildings of libraries,
allowing users to “walk” among them, freely associate with
the virtual space, and obtain a three-dimensional and re-
alistic user experience. When users “walk into” the virtual
space of the library, they can understand the overall layout of
the library and can also obtain information consulting
services and browse the operation mode of the library in the
most direct way of expression, allowing them to understand
at a glance and fully embody the superiority of the smart
library. It also appropriately compensates for the one-sided
and localized information obtained on the library website
and increases the user’s affinity for the virtual reality of the
smart library. e application of visualization technology in
smart libraries can realize the service functions of explicit
tacit knowledge, clarification of fuzzy knowledge, and
concrete abstract knowledge. In the process of smart library
services, the collection of knowledge and the organization of
knowledge are inseparable from the service criteria of
“knowledge visualization.” erefore, the smart library must
achieve the visual effect of vivid knowledge services. Data
visualization technology is conducive to processing intricate
data relationships, transforming data dimensions into visual
dimensions, and then mining the knowledge structure and
development trends hidden in the data.
2.2. Application of Virtual Reality Technology in Smart
Library. In the actual application process, the manifestation
of virtual reality technology can be divided into three types
according to the user’s “immersion” and “interaction.”
2.2.1. Virtual Reality Technology Display Mode Based on
Computer Screen Display. e real-world image or video
captured by the camera equipment is input to the computer,
synthesized with the virtual scene generated by the computer
graphics system, and output to the screen display. e user
sees the final enhanced scene picture or video on the screen.
is form of expression is simple, in the enlightenment stage
of the concept of virtual reality technology, and most of the
things shown to users are display attributes, unable to
operate and interact, and cannot bring much immersion to
users. e implementation scheme of the virtual reality
technology system based on computer screen display is
shown in Figure 2.
2.2.2. Display Mode Based on Optical Perspective. is type
of virtual reality technology needs to use display devices that
emphasize the user’s vision and tactility, mainly helmet and
glasses-type displays, to enhance the visual immersion by
being close to the user’s body. Early AR products produced
Application layer
Smart computing layer Smart
application
Smart
communication
Smart
transmission
Wisd om
perception
Smart application
Personalize
Wisdom space Visualization
Information
retrieval
Smart transmission
Wireless coding Fusion of wisdom Communication Data
fusion
CommunicationTransmission awareness Semantic analysis
MPP
data
Sensing device
Perceptual parameter Sensor
Communication between the transmission layer and the perception layer
Storage device Library browsing
equipment
Terminal layer
Sensor Browser RRC Video monitor
Figure 1: Architecture of the intelligent library information retrieval technology model construction system.
4Complexity
by electronic product companies such as Sony and Google
are geared towards users in the form of helmets and glasses.
Different from the computer screen display, the user can
directly see the external real world. In fact, it uses a
transparent optical synthesizer to project the signal of this
virtual image to the eyes, and then because the real world is
clearly visible, there is no way through shooting processing
and artificial display. Its operating system is shown in
Figure 3.
User experience design includes information construc-
tion, user interface design, human factor configuration
design, and user experience evaluation. In order to complete
these designs, they must be carried out in stages according to
the requirements of user experience design, namely, the
discovery of information needs based on user experience, the
construction of user-oriented services based on resource
integration, and feedback control based on information
integration and service integration. ese three aspects can
be subdivided, as shown in Table 1.
e integration of information resources based on user
experience directly faces users, highlights needs and services,
and allocates limited resources to the service businesses that
users care about most to ensure the efficiency of resource
utilization. To do this, you need to analyze the information
needs of users. e discovery process is to understand and
analyze users’ information needs from multiple angles. e
measures taken include analyzing the current environment,
understanding the real needs of users through surveys or
interviews, deep mining and using the acquired user in-
formation, and coordinating user needs with the service
organization’s strategy and environmental requirements and
then enter the design phase of user experience construction.
Construction design is the main part of user experience
design, including information architecture, user interface
design, human factor research, and user experience evalu-
ation. Information architecture is a high-level information
design that focuses on the organization and presentation of
information, and its purpose is to provide users with clear
and understandable information. User interface design re-
quires reasonable arrangement of interface elements on the
basis of information construction, distinguishing the im-
portance of information, expressing information in an easy-
to-understand manner, and enabling users to interact with
system functions. Human factor research and user experi-
ence evaluation are generally combined. eir responsibil-
ities include user experience testing, researching user
development, communicating with users, and passing these
results to designers. e feedback control design requires the
specification and description of the design process and user-
oriented business, listening to opinions in many aspects, and
designing a user-oriented integrated service feedback con-
trol system to ultimately improve the user experience design.
2.3. Virtual Reality Technology Improves the Effectiveness of
Information Retrieval Services and Management of Smart
Libraries. As the current popularity of virtual reality tech-
nology is not very high and it is not currently included in the
development plan of the library business, the survey of this
technology in the questionnaire must first start with the
cognitive level of the surveyed, and it is easy to be surveyed.
e questionnaire was designed based on the expression of
the reader’s understanding, and the cognitive results are
shown in Table 2.
e survey results showed that 35 people did not un-
derstand the concept of virtual reality technology at all,
accounting for 36.55% of the total number of people in the
survey. Because the cognitive problem of the survey is also
an important reference data, I do not think that choosing
this option will lead to a whole answer. is questionnaire
is invalid, which also reflects the current state of under-
standing of this technology in the industry to a certain
Real
Virtual image
generation
Virtual icon
Real background
Data
Book photo data
Virtual reality
Virtua l obje ct
Camera system
Visual display
No
Ye s
Information
retrieval
Virtua l phot o
Wisdom library
Virtua l book
Display mode
Figure 2: Operating principle model of virtual reality technology display mode based on computer screen display.
Complexity 5
extent, and even if you do not understand the concept of
virtual reality technology, you can express the expectations
of the surveyed in the subsequent survey options. At the
same time, I set the option of “scan QR code” in the re-
search options. In fact, although the method of using QR
code is similar to virtual reality technology, the two are not
in a mutually compatible relationship. Codes do not belong
to virtual reality technology. In the survey results, 14 people
chose the QR code option alone, accounting for 14% of the
total number of people. It can be understood that these
users do use QR codes but they are still familiar with virtual
reality technology. If combined with users who do not
know anything about it, the proportion of respondents who
have a weak awareness of virtual reality technology reaches
50.6%, which accounts for about half. is ratio reflects the
current use of virtual reality technology in books. Among
the remaining options, “scanning reality” is one of the main
features of virtual reality technology, “electronic games” is
a hot development field of current virtual reality tech-
nology, and “available for life services” means that all types
of technologies including virtual reality technology are
available. e selected development goals and those that
chose these options also accounted for the other half of all
surveyed objects, reflecting the controversy of virtual re-
ality technology in libraries.
e combination of virtual reality technology and library
management and services has not only obtained theoretical
support in the previous chapters but also should find the
advantages of the combination of the two in real applications
to enrich theoretical research and lay a foundation for
popularization of practice. is section summarizes specific
advantages by combining the results of the questionnaire
survey and the author’s reasonable assumptions. e survey
results are shown in Table 3.
In the questionnaire survey on the advantages of virtual
reality technology in library applications, 83 people think
that virtual reality technology can provide more compre-
hensive services for library readers, accounting for 90.31%,
and 73 people think it will enhance the interaction between
the library and readers’ sex, accounting for 77.43%, 57
people think that this will make paper books break the limit
of paper media, accounting for 60.23%, 50 people think that
virtual reality technology in the library will help library
management, accounting for 55.9%, and 5 people think the
advantage is not obvious, accounting for 4.4%. From the
analysis of the above survey results, it can be seen that service
is the primary consideration in the application of virtual
reality technology in smart libraries, and its advantages can
be better reflected in services. ere is a certain gap between
the research objects in management and the selection of
services. is also shows that the proportion of virtual reality
technology in the development of smart library services and
management may also fit the selection of this survey.
Table 1: User experience design process.
User needs Experience building Feedback control
Background analysis Concept + first prototype Design description
User interview Site map Process feedback
User role and plot setting Interactive model Performance control
Brainstorming Idea refinement
Group design practice User interface + visual design
User experience evaluation
Table 2: User perception statistics.
Variable Types of Quantity Percentage
Awareness
Don’t understand 35 36.55
Scan the QR code 34 38.71
Sweep the reality object 34 35.47
Electronic games 28 31.17
For life 35 36.58
Virtual graphics
generation
Wisdom books
Virtual display
Smart library Information retrieval visualization
Virtual reality environment recognition
Tri g g er
Information
retrieval
Visualization
equipment
Virtual graphics
Environmental
recognition
Wisd om
information
Recognition
induction
Real world
Vir tual
recognition
Recognition
perception
Perceptual
trigger
Retrieval
visualization
IIC SDIO
Figure 3: Display mode based on optical perspective.
6Complexity
e number of users of smart libraries is large, and the
level of information retrieval service is at the forefront of the
times. It is the basis for smart libraries to adopt and pop-
ularize new technologies, and it is the main advantage.
Compared with the more expensive and fixed-location
navigation machine in the library, the virtual reality tech-
nology can be realized on the user’s mobile phone, and the
service can be provided through the design of the program,
and the user’s use method is flexible, easy to learn, and fast.
As far as the development of smart libraries is concerned, the
introduction of intelligent information services such as
virtual reality technology improves service efficiency and
implements the reader-centered concept. In the question-
naire survey, more than 75% of the respondents believe that
virtual reality technology has improved the interaction
between the library and the reader, and the smart library has
the interaction between the reader and the smart library in
each part. e application of virtual reality technology will
make the way of interaction more intelligent. In terms of
social value, if the use of virtual reality technology is pro-
moted by the smart library of colleges and universities and
the concept of new technology is popularized among young
users, it will make college students who are good at using
computers and mobile phones take advantage of this
technology. e strong impression will also affect their
future scientific and technological concepts and thus they
will have a sense of dependence on the smart library and
become loyal users. e group of young people is a group
with a rapid popularity and a large number. It can also
achieve certain effects in public libraries with evenly dis-
tributed age groups. Providing virtual reality technology
services for older and traditional library user groups can
achieve the effect of narrowing the social technological gap
and completing large-scale technical literacy.
Any new technology may be a double-edged sword in the
initial stage of application. rough the understanding of the
functional characteristics of virtual reality technology,
combined with the learning and understanding of the li-
brary, and the questionnaire survey and analysis, it is also
found that the following points may limit its development,
and user survey results are shown in Table 4.
In the questionnaire survey on the limitations of the
application of virtual reality technology in libraries, 61
people think that the equipment of virtual reality technology
is more expensive, accounting for 64.53%, 44 people think
that the utilization rate of virtual reality technology in li-
braries is low, accounting for 48.38%, 28 people think that
virtual reality technology is difficult to popularize as a new
thing, accounting for 31.17%, 25 people think that virtual
reality technology is complicated and difficult to use,
accounting for 25.82%, and 5 people think there are other
reasons, accounting for 4.5 %. erefore, it can be seen from
the survey results that the cost issue is the main issue in the
application of virtual reality technology in the library. is is
not only in the aspect of virtual reality technology. e
library’s funding is based on the overall consideration of
each part of the library, so virtual reality of the cost of
technical equipment should be carefully considered.
Without experiments, the utilization rate is also worthy of
attention. e low utilization rate of advanced and expensive
equipment after the introduction will cause great losses to
the library. e limitations are discussed in detail below.
3. Knowledge Service Extension and
Information Retrieval Visualization of
Smart Library
As a valuable and high-quality high-level knowledge service
product, smart service is a perceptible, calculable, and vi-
sualized creative service, which will drive the technological
upgrading, conceptual innovation, management reform, and
service transformation of smart libraries, e library be-
comes an incubator for technological innovation and cre-
ativity and promotes the burst of imagination and creativity
of users.
3.1. Characteristics of Knowledge Service Extension of Smart
Library. e ultimate goal of the extension of the knowledge
service of the smart library is to improve the knowledge
service capability and level of the smart library and meet the
increasingly diverse and individual needs of users. Specifi-
cally, the extension of the knowledge service of the smart
library is to use the Internet of ings technology to realize
the digitization of resources, the application of metadata
harvesting, and the establishment of a data warehouse; as
well as the storage and calculation of big data on the basis of
data interconnection, forming a first-hand reliable infor-
mation resources; on the basis of information collection,
resources are reorganized, a knowledge base system through
resource reengineering is built, and the knowledge of in-
formation is realized; a precise service platform based on
situational awareness is built, and personalized knowledge
service products is provided; the intelligence of knowledge is
realized, and finally big data analysis tools are used to
perform machine learning, mine user preferences, recom-
mend personalized knowledge products, and achieve precise
services. e goal of the extension of the knowledge service
of the smart library is shown in Figure 4.
Table 3: User advantage selection.
Variable Types Quantity Percentage
Advantage type
Full service 83 90.31
Improve interactivity 73 77.43
Breakthrough paper media 57 60.23
Improve management effectiveness 50 55.9
e advantage is not obvious 5 4.4
Complexity 7
As shown in Figure 4, the interconnected context is the
technical guarantee for the knowledge service context
function of the smart library. e accessibility and pop-
ularization of the interconnected context depend on the
supportive context technology of the Internet of ings
and the ease of use and audience perception of the
knowledge service platform. Resource context is the
material basis of knowledge services in smart libraries.
Resource reorganization and resource reengineering to
improve the quality of resource content are related to the
smooth development of user knowledge mining, knowl-
edge association, knowledge utilization, and knowledge
creation. In particular, human resources have become the
first resource for the extension of knowledge services in
smart libraries. It is the key to applying emerging tech-
nologies in the process of knowledge services and carrying
out resource reengineering, knowledge creation, and
smart services. e service context is the integration of the
elements of the knowledge service field of Unicom’s smart
library under the combined effect of the technology in-
terconnection context and the resource context. e
personalized service and precise adaptation incentive
function reflect the final performance of the knowledge
service of the smart library. e interconnected context,
resource context, and service context are cross-integrated
and interacted in the knowledge service extension
mechanism of the smart library, which together influence
and determine the knowledge service level and users’
perceived experience and satisfaction.
3.2. Endogenous Power Mechanism Model of Knowledge
Service Extension of Smart Library. Based on the mechanism
of interaction between the user and the smart library
knowledge service system and the influence of the inter-
action function of the smart library knowledge service
system on user behavior, this model is constructed from the
different context dimensions of the interaction process
between the user and the smart library knowledge service
system. Users are the most basic and active force in the
extension of knowledge services. e function of the
knowledge service system of the smart library and the in-
fluence mechanism of user behavior are the user context,
which mainly includes user needs and user experience. e
three knowledge service context factors of the smart library
are interconnection context, resource context, and service
context. e corresponding ones are the ease of use, use-
fulness, and motivation of the correlation between the
knowledge service function of the smart library and the user
information interaction behavior. ey include the ease of
use of the space-time system, the ease of use of the sup-
porting system, the usefulness of the resource construction
system, the usefulness of the resource reengineering system,
the standardized incentives of the service system, and the
personalized incentives of the service system. e extension
of the knowledge service function of the smart library and
the construction of the mechanism model of user behavior
are shown in Figure 5.
It can be seen from Figure 5 that user experience in-
teraction and user demand promotion are accompanied by
Intelligent generation
Information
collection
Knowledge
building
Data
interconnection
Information
situation
Interconnect ed
scenarios
Optimization
Service
extension
Personalized recommendation
Focus on service
End
Smart service Information
resource
Information retrieval space
Smart recommendation
Smart recognition
Figure 4: Goal-oriented diagram of the knowledge service extension of the smart library.
Table 4: Users’ views on limitations.
Variable Types Quantity Percentage (%)
Types of limitations
Expensive 61 64.53
Low utilization 44 48.38
New things are difficult to popularize 28 31.17
Complex operation 25 25.82
Other reasons 5 4.5
8Complexity
both service experience characteristics and demand pro-
motion functions. e interactive function of the knowledge
service system of the smart library and the user’s infor-
mation interactive behavior work are through the following
mechanism: the information of the knowledge service is
presented to the user, and the user confirms the occurrence
of the interactive behavior through self-perception. e
psychological feelings of cognition, emotion, value, and so
forth obtained by users through interactive behaviors are the
result of comparison between user interaction perception
and expectations, which directly affect the evaluation of
knowledge service system functions and service incentive
levels. In the process of information interaction, the user’s
impressive experience is accompanied by use and operation
behavior, including the mastery of the knowledge service
context function and the realization of needs; the user will
always be based on the interactive experience and perception
of the received customized information feedback and adjust
their information interaction behavior. e functional ele-
ments of the knowledge service interconnection context,
resource context, and service context of the smart library
enhance and promote the data interconnection, knowledge
construction, and wisdom generation of users, which have
an impact on users’ information interaction behavior,
stimulate users’ interactive interest at any time, and induce
users’ information interaction. e result of its interactive
experience is changed. Users’ knowledge needs to have new
characteristics such as interconnection, sharing, knowledge
integration, ubiquity, intelligence, and innovation. e
user’s interactive behavior in the knowledge service coin-
cides with the ease of use of the knowledge service inter-
connection context, the usefulness of the resource context,
and the motivation of the service context; that is, the user’s
psychological feelings and satisfaction with the knowledge
service and reflecting a good experience of knowledge
service quality is the whole process of the effect of the en-
dogenous power of the knowledge service extension of the
smart library.
e application of virtual reality technology in smart
libraries has certain feasibility. First, the integration of the
“three networks” and the construction of the Internet of
ings provide networks and traditional equipment and
provide hardware support for the use of virtual technology.
e integration of the “three networks” has achieved a high
level of resource sharing, with higher communication speed,
higher communication quality, and stronger communica-
tion security. As an important part of the new generation of
information technology, the Internet of ings has realized
the real connection of things with the help of the Internet.
e development of the Internet of ings will greatly
promote the development of smart libraries and VR tech-
nology. Second, the development of libraries requires the
integration of virtual reality technology. Smart library is a
new type of library facing the future and facing science and
technology. It is an extension and expansion of traditional
libraries. With the development of smart libraries, more and
more information technology will be widely used in its
function realization and service improvement. When VR
technology has shown great advantages and potentials, its
integration has improved the way of information services
and enhanced the visibility and influence of smart libraries.
ird, the characteristics of virtual reality technology are
applicable to smart libraries. e immersion of virtual reality
technology enables participants to exist in the virtual en-
vironment as subjects. e sense of interaction allows
participants to get feedback from the virtual environment
and realize interaction. Imagination will enable participants
to expand more knowledge content through logical judg-
ment, reasoning, and association based on the information
they have obtained. It can be analyzed from the above three
points that the application of virtual reality technology in
smart libraries is very feasible.
3.3. Visualization of Information Retrieval in Smart Library.
For the design of the information visualization model of the
smart library, it must not only conform to the basic system
structure of the smart library but also fully meet the various
requirements of information visualization. erefore, for the
design of the model, the following principles must be met:
(1) Meeting the individual needs of users. e design of
the model should be able to meet the needs of various
users of the smart library, and different users have
different preferences for the choice of visualization
Intelligent generation
Knowledge building User search
Smart data
Service scenario
Data interconnection User needs
User experience
Wisdom library
Knowledge chain
Information retrieval
Space system Smart library
Information
retrieval
Service extension
library
Figure 5: Mechanism model of endogenous power and user behavior of the extension of knowledge service in smart libraries.
Complexity 9
methods. e personalization of user preferences is
mainly in the results, that is, the way to provide
personalized visualization results through the icon
library.
(2) Universality. e information resources contained in
a smart library are massive, and there are many types
of these information resources, including text,
graphics, images, sounds, and videos. In the process
of model design, it should be classified according to
the information resources contained in the smart
library to avoid the phenomenon that only infor-
mation in a specific field is effective, so that the
model can be used in a wider range of fields.
(3) Convenience. Convenience here means that when
the user visits the smart library, its visual interface is
friendly and there is good interaction between the
user and the system.
(4) Interoperability. Information visualization technol-
ogy faces the massive collection of information in
smart libraries. is information is stored in various
databases in different formats. e visualization
system should be able to achieve undifferentiated
access to information. In addition, the information
contained in the smart library comes from different
application fields, and the system should also im-
plement interoperability between them.
(5) e flexibility of the structure. e development
prospects of visualization technology are unpre-
dictable. Both information technology and computer
technology are developing rapidly, and it is impos-
sible for any technician to fully predict the future
development.
Fuzzy C-means clustering algorithm is a typical infor-
mation retrieval partitioning algorithm; its idea is to make
the similarity between objects classified into the same cat-
egory is the largest, and the similarity between different
objects is the smallest. In the iterative optimization process,
the FCM algorithm continuously updates the values of the
various centers and the elements of the membership matrix
until it approaches the minimum value of the following
criterion function:
Tn(O, P) �
N
j�1
c
i�1
oijw2
ij.(1)
Regarding feature extraction, in the test sample data set,
there are a total of 988 keywords, and the number of keywords
that are different from each other reaches 628. wis the weight of
the number of keywords. After the data dimensionality re-
duction process, the remaining unique keywords are 113, which
greatly reduces the aggregation. e data dimension of the class
algorithm in the document space vector matrix Ris stored in a
text file as the data source of the FCM algorithm in Matlab. e
number of iterations of running the FCM algorithm is 100, and
the clustering result when the clustering objective function value
is the smallest is taken out as the final result output. e results
of the five experiments are shown in Table 5.
e clustering results of the third run in Table 5 are the
best, and only 2 documents are misclassified in each of the
three categories. In general, when the number of FCM
documents is small, the clustering quality is better, with an
average correct rate of 97.4%. But sometimes the result is not
very stable, mainly due to the limitations of the FCM al-
gorithm itself; that is, the random initialization of the center
point has a greater impact on FCM.
Since keywords can directly indicate the subject of the
literature and the characteristics of the subject, the similarity
of the literature can be directly reflected by the similarity of
the keywords. In order to cluster the documents, the doc-
ument similarity matrix is first defined, and the similarity is
expressed by Euclidean distance. e following definition is
based on the following assumptions: suppose that the total
number of documents to be clustered is n, the total number
of different keywords in all documents is m, the keyword set
is S, and the number of categories is k. e document
similarity matrix (n∗n) is defined as
W11, W12 ,. . . , W1n
W21, W22 ,. . . , W2n
. . .
Wn1, Wn2,. . . , Wnn
⎡
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎢
⎣
⎤
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎥
⎦.(2)
e similarity of two keywords is defined as
Wλi,λj
�
κ
κi+κj−κ, k ≥4,
0,κ≺4.
⎧
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎩(3)
e components of the literature keyword matrix are
defined as
Qij �max Wλi,λj
.(4)
e clustering objective function is defined as follows:
E�k
j�1m
i�1yi−yj
2
mj
.(5)
e selection operation is adopted to select good indi-
viduals from the current group and decide which individuals
can enter the next generation. First, the individuals are
sorted according to the fitness function from large to small,
and the first hindividuals are copied as new individuals
directly into the next generation, and the fitness of the
remaining individuals is calculated as follows:
P(D) � c+(d−c)M−rand(D)/M−d
(M−c)
.(6)
In order to test the feasibility and effectiveness of the
information retrieval of the smart library, the value of 207
documents in the life is taken as the test data set. e genetic
algorithm parameter is 50, the probability of mutation is
represented by P, the value is 0.15, and the intersection value
is 0.0002. e value is 0.76, and the maximum number of
iterations is represented by T, with a value of 100. After 50
10 Complexity
experiments, the result of the average objective function is
shown in Figure 6.
It can be seen from Figure 6 that, in the iterative process
of the FCM algorithm, the objective function can converge
quickly, and the value of the objective function is greater
than that of the GA algorithm, indicating that the conver-
gence accuracy of the FCM algorithm is inferior to the GA
algorithm. GA algorithm converges slowly, but the average
accuracy of classification can reach more than 99%, which is
better than FCM algorithm. As the GA algorithm draws on
the idea of genetics in biology, it searches for the optimal
solution through repeated iterations of “survival of the fit-
test.” erefore, the optimization ability of GA is better than
the FCM clustering algorithm, but the disadvantage is that
the GA calculation speed is slow and many parameters need
manual intervention.
3.4. Information Retrieval Visualization Construction. For
the design of the information visualization model of the
smart library, it must not only conform to the basic system
structure of the smart library but also fully meet the various
requirements of information visualization. erefore, for the
design of the model, the following principles must be met:
(1) Meeting the individual needs of users. e design of
the model should be able to meet the needs of various
users of the smart library, and different users have
different preferences for the choice of visualization
methods. e personalization of user preferences is
mainly in the results, that is, the way to provide
personalized visualization results through the icon
library.
(2) Universality. e information resources contained in
a smart library are massive, and there are many types
of these information resources, including text,
graphics, images, sounds, and videos. In the process
of model design, it should be classified according to
the information resources contained in the smart
library to avoid the phenomenon that only infor-
mation in a specific field is effective, so that the
model can be used in a wider range of fields.
(3) Convenience. Convenience here means that when
the user visits the smart library, its visual interface is
friendly and there is good interaction between the
user and the system.
(4) Interoperability. Information visualization technol-
ogy faces the massive collection of information in
smart libraries. is information is stored in various
databases in different formats. e visualization
system should be able to achieve undifferentiated
access to information. In addition, the information
contained in the smart library comes from different
application fields, and the system should also im-
plement interoperability between them.
(5) e flexibility of the structure. e development
prospects of visualization technology are unpre-
dictable. Both information technology and computer
technology are developing rapidly, and it is impos-
sible for any technician to fully predict the future
development. erefore, when designing a visuali-
zation system, full consideration should be given to
the standardization of the database and the expan-
sion of the system.
e information visualization model of the smart library
designed in this paper is shown in Figure 7, which is based
on the combination of the basic architecture of the smart
library and the reference model of information visualization.
As shown in Figure 7, the proposed smart library infor-
mation visualization model includes a total of six modules:
source data module, original database module, feature
Table 5: FCM clustering results.
Number of experiments
Information science
(52) Philology (71) Library science (87) Objective function
Correct Error Correct Error Correct Error
1 47 2 68 1 84 2 101.2245
2 48 4 71 2 83 3 100.4342
3 51 2 69 2 84 2 98.9119
4 48 3 67 2 83 3 102.5346
5 51 2 67 3 84 2 99.2325
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Average objective function
123456789100
Number of iterations
GA
FCM
Figure 6: Comparison of the relationship between objective
function and evolutionary algebra.
Complexity 11
database module, view object module, visualization interface
module, and extended function slot module.
Based on the above considerations, the L-Apriori al-
gorithm is described as follows:
(1) Classify the collection of books, combine the book
classification method, and divide the collections into
professional books and cross-professional books
according to their professional background. Pro-
fessional books for each profession are definitely
different.
(2) Preprocess the historical borrowing data, and set up
subdatabases according to profession and year. Each
subdatabase only contains the historical borrowing
data of previous students in the major, which relates
to the borrowing information of professional books
and cross-professional books.
(3) Data association rule mining of professional books:
data mining is performed on subdatabases through
Apriori algorithm; then the frequent item set A of
each subdatabase must be the borrowing informa-
tion of this professional book, and association rules
can be extracted for recommendation; at the same
time, get the frequent item set B of cross-professional
books.
(4) Prune the frequent itemsets obtained by Apriori
algorithm mining, delete the frequent itemsets of
professional books, and keep only the frequent
itemsets of cross-specialties.
(5) Compare the frequent itemsets A of the first sub-
database a and the frequent itemsets B of the second
subdatabase b, find out the same parts, and put the
same parts into the frequent itemsets C of the merged
new data category c.
(6) For frequent itemsets, scan b to obtain the support
degree supx in b and supx plus the support degree
sup in A; if the sum of the two is greater than or equal
to the minimum support degree, put them in
C. Similarly, for frequent itemsets, scan a and
recalculate its support; and if it is greater than or
equal to the minimum support, put it in C.
(7) Repeat steps 5 and 6 until all the subdatabases are
merged to form a new frequent itemset and
Search found
Original database
SDN
Visual interface HMI
Search term
Wisdom naming
Smart library
Retrieval
analysis
Icon library
Vie w
image Security
monitoring
Interface
visualization
Feature extraction
Wisd om
extraction
Source data
Wisd om
resource
Smart
analysis
Internet
Figure 7: Information visualization model of smart library.
12 Complexity
association rules, and the extracted association rules
are recommended to users in a visual way.
4. Experimental Verification
In order to verify the efficiency of the L-Apriori algorithm, a
comparative test was conducted with the Apriori algorithm.
e test data is the student borrowing data of a smart library
from 2011 to 2012 in the last semester. e test results and
the required time are observed through different mining
algorithms. e required software and hardware environ-
ments are the same. e performance comparison of the two
algorithms is shown in Figure 8.
rough the comparison and analysis of the test results
and the required time, L-Apriori is more effective than
April in the frequent itemset mining of library borrowing
data, and the result is simpler, mainly because the L-Apriori
algorithm pruned some of the frequent items of profes-
sional books in the process of subdatabase frequent itemset
integration, and the pruned frequent items of professional
books directly extracted the association rules and recom-
mended them to users in the form of knowledge. e
frequent items of cross-professional books are integrated
one by one to form a frequent itemset of cross-professional
books, which is much less than the frequent itemset ob-
tained by April and naturally requires less time. Both the
cross-professional book association rules obtained by the
L-Apriori algorithm and the professional book association
rules obtained by the Apriori algorithm can be recom-
mended to users.
e recommendation service methods of the interviewed
smart libraries that provide recommendation services can be
divided into two types: personalized recommendation and
nonpersonalized recommendation. As shown in Figure 9,
about 56% of the interviewed smart libraries provide non-
personalized recommendation services, such as “new book
recommendation,” “borrowing ranking,” “hot review
books,” “librarian recommendation,” and “reader recom-
mendation.” Only about 23.5% of the provincial smart li-
braries have achieved personalized recommendations, but
about 72% of them need to rely on third-party search
systems to complete personalized search recommendations.
Personalized search recommendations mainly rely on user
search content to make recommendations, with similar
recommendation forms and relatively single content, such as
“Guess you like,” “Related Borrowing,” “Related Collection,”
“Other Works by Authors of the Same Name,” and “Bor-
rowing Relationship Diagram.” e survey found that the
third-party systems that provide personalized search rec-
ommendations are mainly the ILAIII knowledge portal
search system and the Interlib system. e smart library has
completed personalized recommendation under its own
recommendation system. When a user logs in to the service
platform of the smart library, the recommendation system
can analyze the user’s interest and preferences based on the
user’s historical data, provide personalized recommenda-
tions of “Guess you like” on the homepage, and provide
nonpersonalized recommendations of “everyone cares” or
dynamic recommendation.
Using the information retrieval visualization API, event
processing of the visualization interface can be performed,
and interactive control can be added. is means that
multiple visualization views can be coordinated, and data
flow and control flow can be managed with the server
through event processing. Information retrieval visualiza-
tion provides more than 20 chart types, including the chart
type that comes with the API and many visualization chart
types developed by third parties, such as tag clouds.
erefore, with the increase of the API applications, the
optional visualization types will gradually increase. As
shown in Figure 10, the view elements are manipulated by
responding to mouse click events. e information retrieval
visualization API can also call the information retrieval
visualization API to update the visualization view through
asynchronous interaction with the server without updating
the entire page.
However, from the perspective of the types of visuali-
zation, there is a lack of many classic views in the field of
information visualization, such as graph-based visualization
types and many tree-based visualization views (e.g., hy-
perbolic trees, radial trees, etc.). It lacks focus + context and
overview + detail visualization.
e implementation of questionnaire survey mainly
includes two parts: questionnaire survey and individual
interview. All the users who participated in the question-
naires selected in this article are those who have used mobile
virtual reality technology and are between 20 and 40 years of
age. e author first explained the basic concepts of mobile
virtual reality technology and then briefly introduced the
mobile virtual reality technology prototype system and its
functions designed and implemented. Finally, the software
was installed on the mobile phone with the Android op-
erating system and the subjects personnel conduct operation
and experience. A total of 55 questionnaires were distributed
in the survey and 55 were returned, of which 48 were valid
questionnaires, with an effective rate of 95%. After collecting
the questionnaires, the author randomly selected several
questionnaires and conducted individual interviews with the
corresponding subjects to discuss related issues that need to
be understood.
It can be seen from Figures 11 and 12 that the four
categories of user experience scores are not high, the highest
score is behavioral experience, with 55 points, and users are
basically satisfied. Since the designed mobile virtual reality
technology prototype does not involve a social module, users
will basically not have a social experience, so the social
experience score is low and users are very dissatisfied.
In order to verify the clustering performance of the
information retrieval visualization algorithm, the Interna-
tional Standard (IRIS) classification data is used for testing.
e data set uses the characteristics of the Orioles as the data
source. e data set contains 150 data sets, divided into 3
categories, each with 50 data, and each data contains 4
attributes.It is very commonly used in data mining and data
classification for test set and training set. In the PSO al-
gorithm, the inertia weight and learning coefficient adopt
linear change strategy. In the information retrieval visual-
ization algorithm, the number of IS-PSO iterations is 20, and
Complexity 13
Running time (s)
50
100
150
200
250
300
Running time (s)
16 18 201412
Minimum support (%)
0
50
100
150
200
250
Aprior
L-Aprior
(a)
×104
16 18 201412
Minimum support (%)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Running time (s)
0
2000
4000
6000
8000
10000
Running time (s)
Aprior
L-Aprior
(b)
Figure 8: Performance analysis of L-Apriori algorithm.
Nonpersonalized recommendation
Personalized recommendation
No recommendation
Figure 9: Specific content of the recommendation service provided by the smart library.
Wor k
Search
Comm
Reading
Browse
(a)
Wor k
Search
Comm Reading
Browse
(b)
Figure 10: Pie chart (view events responding to mouse click).
14 Complexity
the number of FCM iterations is 80. e results are com-
pared with the results of IS-PSO algorithm and FCM al-
gorithm at 100 iterations. e results of multiple tests of the
two algorithms on the same data set are relatively stable.
Table 6 shows the comparison of the average performance of
the three algorithms.
From the perspective of the correct rate of classification,
the correct rate of FCM algorithm classification is only
88.68%, while IS-PSO and information retrieval visualiza-
tion algorithms have reached a higher accuracy rate; from
the perspective of running time, IS-PSO runs the most due to
the complexity of the algorithm.e shortest running time of
the FCM algorithm is more than 17 seconds, while the
running time of the information retrieval visualization al-
gorithm is in the middle. From the objective function, the
information retrieval visualization algorithm is the basis for
finding a better center point in the PSO algorithm. On the
other hand, with the advantage of fast convergence of FCM,
the optimization accuracy is the best, while the IS-PSO
algorithm could not exceed the information retrieval visu-
alization algorithm in the optimization accuracy due to the
limitation of running algebra.
Figure 13 shows a simulation diagram of the objective
function optimization of the two algorithms. e FCM al-
gorithm has the fastest convergence speed but is premature.
e IS-PSO algorithm has the slowest convergence speed
and the worst optimization performance within the range of
100 iterations. e information retrieval visualization al-
gorithm is consistent with the IS-PSO algorithm in the first
20 generations, but in 20 after the first generation, due to the
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Satisfaction
161068 180142124
Smart library experience
Figure 11: Histogram of satisfaction with various elements of user experience.
360412 5
Experience category
0
10
20
30
40
50
60
Satisfaction
Basic experience
Cognitive experience
Emotional experience
Behavior experience
Figure 12: Histogram of user experience category satisfaction.
Complexity 15
FCM algorithm, the convergence speed is accelerated, and a
better optimization effect can be quickly achieved.
As shown in Figure 13, although the efficiency of in-
formation retrieval visualization algorithm is slower than
FCM, its optimization performance is better than FCM. e
information retrieval visualization algorithm combines the
advantages of higher optimization accuracy of PSO and the
characteristics of fast convergence of FCM, so the infor-
mation retrieval visualization algorithm can be better ap-
plied to document clustering in smart libraries.
5. Conclusion
tInner motivation is the decisive force to promote the
increase of contextual functions of new knowledge services.
e key to exploring the extension and sustainable de-
velopment of knowledge services is to explore the intrinsic
motivation of new knowledge service enhancements.
erefore, it is necessary to comprehensively consider the
development and changes of user experience and per-
ception in the knowledge service process, continuously
improve the user’s interactive experience and perception,
continuously improve the new knowledge service context
function of the smart library, and comprehensively and
evenly improve the new knowledge service context func-
tion design and development to better meet the multidi-
mensional applications and individual needs of users.
External motivation is the driving force and supporting
force to promote the contextual function of new knowledge
services, which mainly comes from the improvement of
resource content quality and basic conditions. Resource
context is an important force that determines the function
of the new knowledge service, and the maximization of
resource content attributes and value is the basic force to
promote the interactive function of the new knowledge
service context. According to the internal mechanism of
knowledge service extension of smart library, the internal
power and external power are actively and effectively used
to form a dynamic and balanced situational state in order to
promote the improvement of the overall function of the
library’s new knowledge service. By building a smart
technology system of smart perception layer, network
transmission layer, data resource layer, and smart appli-
cation layer, with the help of various sensing devices in the
perception layer, network technology in the network
transmission layer, data mining and cloud computing in
the data resource layer technology, virtual reality and
augmented reality technology at the smart application layer
complete the collection, distribution, and organization of
user data and collection data and realize user personalized
recommendation services, scene-based services, multime-
dia services, smart space services, visualization services,
and virtual realistic service. However, smart libraries also
need diversified smart countermeasures to solve various
problems in the construction process and establish an
interactive smart platform for the multifunctional smart
library system to check for deficiencies and make up for the
lack of public welfare services. Under the banner of ad-
vocacy, we can focus on user needs and implement the
service standards of “precise demand and high-quality
services” to create a smart space, to realize the intercon-
nection of physical space, to increase the comfort and
humanistic care of the space, and to embody the concept of
a smart library for the convenience of readers and green
development. In addition, the integration of online and
offline libraries, libraries and bookstores, libraries and
logistics companies, integrated service solutions, and other
integrations are also being further explored, and integra-
tion is becoming the main form of the development of
smart libraries.
Data Availability
e data used to support the findings of this study are
available from the corresponding author upon request.
Conflicts of Interest
e author declares that there are no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Table 6: Comparison of average performance of algorithms.
Algorithm Correct rate (%) Operation hours Objective function
FCM algorithm 88.68 0.138668 0.0515
IS-PSO 94.66 18.358748 0.0485
Information retrieval 96.2 3.964959 0.0384
Objective function value
24681012140
Number of iterations
0
0.05
0.1
0.15
0.2
0.25
0.3
FCM
PSO
Information retrieval visualization
Figure 13: Simulation diagram of objective function.
16 Complexity
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