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Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
Applied Mathematics and Nonlinear Sciences
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†Corresponding author.
Email address: nlu1006@163.com
ISSN 2444-8656
https://doi.org/10.2478/amns-2025-0515
© 2025 Lulu Qu and Zhigang Cheng, published by Sciendo.
This work is licensed under the Creative Commons Attribution alone 4.0 License.
Digital Museum Interaction Design Based on Artificial Intelligence and User Role
Modeling
Lulu Qu1,†, Zhigang Cheng2
1. Anhui Sanlian University, Hefei, Anhui, 230000, China.
2. Anhui Provincial Urban and Rural Planning and Design Research Institute Co., Ltd., Hefei, Anhui,
230000, China.
Submission Info
Communicated by Z. Sabir
Received October 4, 2024
Accepted January 30, 2025
Available online March 19, 2025
Abstract
This project takes the development trend of museum digitization as the foundation, classifies the user groups, constructs
the user role model, and selects the demand elements of digital museum interaction design through user interviews. The
fuzzy KANO model is introduced to classify the selected demand elements of digital museum interaction design and
summarize the hierarchical ordering of digital museum functional elements. On this basis, the interaction design of
digital museum is carried out by taking Sanxingdui as an example, and the evaluation of the design effect is completed
through test experiments. The results show that the selected 22 user requirements are categorized into four hierarchical
elements. Most users are able to complete the main task test within 60s, and the usefulness, ease of use, ease of learning
and satisfaction scores of the digital museum design are in the middle to upper level of 7.00~7.55, and the constructed
interaction design of the digital museum can meet the needs of most users. In this paper, the application of augmented
reality interaction design in constructing digital museums based on user role model enhances users’ sense of visiting
experience and satisfaction.
Keywords: Artificial intelligence; User role model; Fuzzy KANO model; Design effect evaluation; Digital
museums.
AMS 2010 codes: 03C65
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
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1 Introduction
With the rapid development of digital technology, digital museums have emerged as an emerging
platform for cultural heritage and education. Digital museum refers to a new type of museum form
that uses modern information technology, especially virtual reality, 3D modeling, multimedia and
network technology, etc., to present the functions and functions of the physical museum in the form
of digitization on the Internet [1-4]. And it successfully breaks through the time and space
limitations of traditional museums, enabling viewers to access and experience the museum’s
exhibition content anytime and anywhere through online platforms [5-7]. Digital museums not only
provide rich digital resources of cultural relics, but also enhance the audience’s viewing interest and
learning effect through immersive and interactive displays, becoming an important platform for
cultural inheritance, education popularization and scientific research [8-11].
The interaction design of traditional museums is often oriented to physical objects, and information
is conveyed through the physical display and graphic introduction of exhibits [12]. However, there
are certain limitations in this display method, in which the audience can only passively accept the
information and cannot deeply understand the connotation and the story behind the exhibits [13-14].
In order to break through this limitation, character models can be utilized to effectively focus the
interaction design. Designers focus their energy on the most valuable user behavioral patterns and
treat the preset personas as real people, so that they can better think and experience from the user’s
perspective, and let them express the user’s goals and needs in the way of specific characters
[15-18]. Through artificial intelligence technology, the historical background, cultural connotation,
production process and other related information of the exhibits are integrated and displayed, so that
the audience can learn more background knowledge while viewing the exhibits, and enhance the
interesting and educational nature of viewing [19-21].
This paper analyzes the development of digital museum interaction based on artificial intelligence,
and researches the interaction between visitors and collections during the visit. Taking the young
and middle-aged group as the target research object, it is divided into three categories of learning,
scientific research, and leisure, and the user role model is constructed. Interviews were conducted
with representative users from different categories, and the functional elements of digital museum
interaction design were selected from six aspects: exhibits, exhibition information, exhibit
communication, guided tours, user-related and others. Subsequently, a fuzzy Kano questionnaire
was designed, and the questionnaire data were collected and statistically analyzed to classify and
confirm the attributes of the functional elements, and to classify the selected functional elements
into different tiers. Based on this, relying on the tangible cultural heritage resources of Sanxingdui,
its digital museum interaction design is explored. Finally, usability testing experiments are
conducted by means of offline practical experience, analyzing users’ task completion time and
satisfaction scale results to explore the design effect of the constructed digital museum.
2 Artificial intelligence-based interaction in digital museums
Artificial intelligence era under the construction of museums and traditional museums have
essential differences in the construction process will be integrated into more intelligent technology,
the physical museum will be transferred to the virtual cyberspace, the formation of digital museums,
but also the integration of technology and the Internet, the formation of the “Internet + Museum”
development model, and the integration of big data technology to enhance the information analysis
capabilities of the museum. The integration of big data technology allows the museum’s
information analysis ability to be enhanced, but also through the implantation of VR technology to
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
3
allow visitors to enter the virtual space and cultural relics close contact. In the process of the
development of digital museum, the main thing is to start with intelligent interaction.
Intelligent interaction is mainly divided into cultural relics management process administrators and
collections interaction, interaction between physical components of the museum, the process of
visitors and collections interaction, the public and the museum interaction several aspects.
Intelligent interaction includes QR code technology, VR technology, big data technology and many
other technologies.
First, the administrator and the collection interaction. After the integration of QR code technology,
the administrator of the collection can know the various requirements for the preservation of the
collection by scanning the QR code, and then it can be transferred to the corresponding storage area
with great precision. Moreover, in the process of collection management, the QR code can also be
used to confirm the identity of the administrator, and if it is found that it is not the administrator, an
alarm will be issued.
Second, the interaction between the physical components of the museum. The entire museum has
intelligence between the components, some components can be disassembled and assembled
according to the need to achieve the interaction between the components. In addition, the weak
electricity system in the building has good intelligence, which can automatically adjust the
functions of monitoring, fire protection, dust control, listening, scanning, etc. according to the
demand, so as to realize the comprehensive security management.
Third, the interaction between visitors and collections in the process of visiting. In the traditional
collection management, it is difficult for visitors to observe the collection comprehensively, the
perspective is incomplete, untouchable, etc. All of them make the visit become defective, and it is
difficult to make visitors’ curiosity and learning psychology to be satisfied. Through the integration
of the corresponding technology, visitors can interact with the collection, visitors can scan the QR
code attached to the collection to understand all the information, have a systematic understanding of
the history of cultural relics, and realize the social education function of cultural relics.
Fourth, the public and the museum interaction. Museums need to promote themselves to the society,
and with the help of 5G technology, the social public outside the museums can get the information
pushed by the museums to understand the characteristics of the cultural relics as well as the
arrangement of the museums’ recent exhibitions, so that more people can interact with the museums
and expand the influence of the museums.
This study combines artificial intelligence technology to focus on the interaction between visitors
and collections during the visit, and conducts research on the interaction design of digital museums.
3 Selection of needs of digital museum user groups
3.1 User orientation
This study of digital museum exhibitions mainly targets young and middle-aged people because
they are the main consumers of smart devices and electronic products, as well as the main venue for
museum visits. In addition, this group is more receptive to new things, and digital products such as
online exhibitions are easily recognized and highly regarded. By targeting this group, the exhibition
design can better achieve the museum’s exhibition goals and enhance the user experience.
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
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3.2 User Role Model
The concept of “user role model” aims to construct virtual characters based on the data obtained
from the preliminary user research to deeply analyze and understand user needs. The purpose of this
study is to systematically analyze and differentiate user groups with different characteristics based
on the relevant data information of museum exhibition users, and to construct a typical user role
model, so as to provide a basis for contextualized cognition. The adoption of user role models can
help researchers to deeply understand user needs, better serve users, and enhance the user
experience of digital museum exhibits.
This paper constructs a user role model for digital museums, divides online exhibition users into
two types of users: leisure and learning, and carries out user clustering from five aspects, including
basic user information, exhibition viewing motivation, behavioral habits, demand characteristics
and emotional experience, and divides users into the following three types.
3.2.1 Recreational users
Leisurely museum users are interested in the display content of the museum, pursuing a more
interesting and interactive display experience, initiating the vivid presentation of exhibits to
stimulate the desire to explore, and also expecting the displays in the museum to be more interactive,
allowing them to be more actively engaged in the exhibition and to gain a more in-depth visiting
and learning experience.
3.2.2 Learning users
Learning-oriented museum visitors pay more attention to the details of the exhibits and the
comprehensiveness of the display content, and expect to be able to obtain more intuitive exhibit
information and story background through multi-angle observation and immersive experience.
These expectations can effectively enhance the efficiency and experience of visiting exhibitions.
3.2.3 Research users
Scientific research users are mostly experts in a certain field or researchers with high cultural level,
and the number of such users is relatively small. These users have a strong sense of purpose, clearly
define the scientific research information they want to find, obtain instant cutting-edge information,
look for knowledge exchange platforms, and pay attention to the comprehensiveness of the
museum’s materials.
The user role model in this study can provide a scientific and accurate user profile for the design
and optimization of digital museum experience, and thus improve user satisfaction and experience.
3.3 Digital Museum Functional Elements
3.3.1 User interviews
After constructing the user role model, the selection of functional elements for digital museum
interaction design is carried out through interviews of three types of user roles, laying the
foundation for the subsequent final calculation using the fuzzy Kano model.
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
5
The first type of interview subjects are learning users, mainly using the museum to obtain
extracurricular knowledge, and two students specialized in design are selected. The second type of
interview objects choose scientific research-oriented users, mainly through the museum to obtain
cutting-edge knowledge and exchange of lecture activities, thus selecting the museum’s scientific
researchers 2 people. The third category of interview subjects are leisure users, mainly using
holidays to visit the museum, thus 4 public users are selected, and a total of 8 people are selected
for this interview.
3.3.2 Functional elements
The functional elements of digital museum interaction design are selected by combining the
functional elements of digital museums studied by previous researchers and the interview results of
different types of user roles. The functional elements of digital museum interaction design are
shown in Table 1, covering six dimensions: exhibits, exhibition messages, exhibit communication,
guided tours, user-related and other dimensions.
Table 1. Functional elements of digital museum interaction design
Function
Functional elements
Symbol
Exhibits
Exhibits basic information
F1
Spread information of the exhibits
F2
Image zooming of the exhibits
F3
3D model exhibition
F4
Relevant audio video
F5
Exhibition message
Exhibition and upcoming exhibition
F6
End of the exhibition and exhibition playback
F7
The time and place of the exhibition
F8
Special information
F9
Exhibit
communication
Community, activities, BBS communication
F10
Share and mail sharing
F11
Praise, comment, collect
F12
Photo download, picture printing
F13
Guide
3d scanning and AR browsing
F14
Exhibition hall map guide
F15
Panoramic view of the exhibition hall
F16
User correlation
Login and registration
F17
National versions
F18
Search
F19
Booking consulting and booking channels
F20
Other
Venter product
F21
Museum development information and so on
F22
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
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4 Analysis of functional requirements for digital museums
4.1 Methods of analysis
4.1.1 Fuzzy KANO modeling
Fuzzy Kano model is derived from the introduction of fuzzy theory on the basis of Kano model,
which follows the basic theory of Kano model, and still divides the factors affecting satisfaction
into five types: mandatory needs (M), expected needs (O), charismatic needs (A), undifferentiated
needs (I), and reverse needs (R).
The fuzzy Kano questionnaire differs from the 0 and 1 in the Kano model evaluation, but is split
into multiple experience data based on the actual user experience in terms of satisfaction and
dissatisfaction. Fuzzy Kano model analysis method is a more practical method of analyzing survey
results invented on the results of traditional Kano structured questionnaires and analysis methods.
The purpose is to categorize and clarify the relationship between the priorities of the user ’s fuzzy
needs through quantitative methods.
4.1.2 Process of analyzing questionnaire data
The acquired fuzzy Kano questionnaire is summarized, analyzed and counted to obtain a
classification table of quality attributes for each demand element. The specific steps are as follows:
1) Establish a fuzzy matrix to generate the interaction evaluation matrix of a respondent’s
design element
W
. Take the following table as an example, respondent
m
for
i
f
,
assuming that has this design element matrix
0.8 0.2 0 0 0X=
and does not have this
design element matrix
0 ~ 0 ~ 0.6~ 0.2~ 0.2Y=
, generates the interaction evaluation
matrix as:
0.48 0.16 0.16
0
0
.12 0.04 0.0
00
00
0 0 0 0 0
0 0 0 0 0
0 0 0
4
0
T
W X Y
==
(1)
2) Combine the matrix results with the fuzzy Kano assessment form to calculate the affiliation
vector for each quality element
T
. The calculation process is as follows:
The required demand element (
M
) in matrix
W
is:
25 35 45 0.04 0 0 0.04
M
S W W W= + + = + + =
(2)
The desired demand element (
O
) in Matrix
W
is:
15 0.16
o
SW==
(3)
The charisma demand element (
A
) in Matrix
W
is:
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
7
12 13 14 0 0.48 0.16 0.64
A
S W W W= + + = + + =
(4)
The undifferentiated demand element
( )I
in Matrix
W
is:
1 22 23 24 32 33 34 42 43 44
0 0.12 0.04 0 0 0 0 0 0.16
S W W W W W W W W W= + + + + + + + +
= + + + + + + + =
(5)
The reverse demand element (
R
) in Matrix
W
is:
21 31 41 51 52 53 54
00000000
R
S W W W W W W W
+
= + + + + +
=++++++=
(6)
The affiliation vector of
i
f
for respondent
m
is calculated as:
10
M4
emb .
ership de ,gree 0
vector ,,,
0. .16 0.64
0 16 0
O
M A I A
f
S
S S S S
T
=
(7)
3) Introduce a confidence level of
. If an element of
T
has a value of
, that element is
represented by a 1, otherwise it is represented by a 0 (if more than one 1 occurs, the values
are taken in order). Thus the vector of demand attributes of respondent
m
for
1f
is
1 (0,0,1,0,0)Tf =
.
4) This process is performed on all questionnaire data and all results are accumulated and
sorted. The design element with a high number of occurrences is the demand category of
that element, and if the cumulative number of occurrences is equal, then the demand
categories are taken in order of importance, and the order is: M>O>A>I>R.
5) Create a table of quality attribute classification results.
4.1.3 Design element satisfaction values
When multiple requirements belong to the same category, there will be requirement attribute
prioritization, i.e., prioritizing or focusing on the design of the requirements, at this time, the
Better-worse coefficient is introduced to conduct the analysis.The Better-worse coefficient indicates
the degree to which a certain feature can increase the satisfaction or eliminate the impact of
disliking. The formula is as follows:
ii
i i i i
AO
SI A O M I
+
=+ + +
(8)
ii
i i i i
OM
DSI A O M I
+
=− + + +
(9)
The SI value represents that if the product provides this feature or service, the satisfaction level
increases and the value is positive. The closer the value is to 1, the faster the user satisfaction rises
when this feature or service is provided, i.e., the higher the priority of this feature or service.
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
8
A DSI value means that if the product does not provide this feature or service, satisfaction decreases
and the value is negative. The closer the value is to -1, the greater the impact on user dissatisfaction,
i.e., the higher the priority of this feature or service.
The horizontal coordinate of the Better-Worse coefficient plot is the absolute value of Worse, and
the vertical coordinate is the absolute value of Better, thus both horizontally and vertically, the
larger the better, the higher the priority.
4.2 Questionnaire
According to the fuzzy Kano model, the functional items in the functional elements of the digital
museum were scored, and a two-way questionnaire on the user needs of the digital museum of the
continuous fuzzy Kano model was made, and the questionnaire asked each functional item in both
directions, that is, when there is such a need, the user’s feelings and when there is no such demand,
the user’s feelings, and the questionnaire answers are in the interval of [1,5], and each integer 1, 2, 3,
4, and 5 represents “dislike”, “acceptable”, “neutral”, “should be”, and “like” respectively”.
A total of 157 questionnaires were recovered through field and online surveys, among which 18
questionnaires were suspicious and 139 questionnaires were valid, and the recovery rate of valid
questionnaires was 88.54%.
4.3 Results of data analysis
4.3.1 Classification of functional elements
According to the evaluation guidelines of the fuzzy Kano model, the results of processing the valid
data are as follows to determine the Kano model categorization of the functional demand items of
the digital museum. According to the steps of the fuzzy comprehensive evaluation method, the
evaluation indexes are quantified to get the Kano category affiliation degree of the functional
requirements of the digital museum, and it is found that when it is 0.4, it can ensure that the
information is not distorted and avoid the information cross.
The results of analyzing the functional elements of digital museums are shown in Table 2.Among
the 22 functional elements of digital museum interaction design, there are 10 elements of
undifferentiated attributes, 6 elements of charismatic attributes, 3 elements of expected attributes,
and 3 elements of reversed attributes. Factors that do not have any effect on the functional
requirements of digital museum interaction design can be excluded.R is the reverse requirement, i.e.,
the requirement that will not have any enhancement on user satisfaction, and even cause
dissatisfaction.360-degree panoramic browsing F16, the versions of each country in the exhibition
hall F18, and the cultural and creative products F21 belong to the reverse attribute requirements.
No-difference factor I is the demand that does not enhance user satisfaction, but it may be a
necessary demand, just that the user does not pay attention to it, and it needs to be analyzed in
specific scenarios.
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
9
Table 2. Analysis results of functional elements of digital museum
Symbol
Membership vector
Categories
A
O
M
I
R
F1
19
35
29
49
7
I
F2
55
48
16
14
6
A
F3
16
22
39
57
5
I
F4
34
51
28
21
5
O
F5
57
33
31
14
4
A
F6
36
52
29
15
7
O
F7
29
32
20
53
5
I
F8
14
30
27
48
20
I
F9
10
26
41
51
11
I
F10
54
32
25
20
8
A
F11
18
29
36
49
7
I
F12
17
59
43
11
9
O
F13
16
36
34
45
8
I
F14
50
39
28
16
6
A
F15
13
23
46
50
7
I
F16
3
29
28
32
47
R
F17
45
32
27
24
11
A
F18
27
18
22
20
52
R
F19
25
32
20
51
11
I
F20
55
34
28
13
9
A
F21
11
30
31
18
49
R
F22
20
23
38
51
7
I
4.3.2 Determination of functional attributes
As A and O are charm attribute and expectation attribute, charm attribute is the attribute that
exceeds the user’s will and makes the user feel satisfied while using the product and expectation
attribute is the satisfaction that the user will feel when his/her expectation is fulfilled. And
Better-Worse coefficient can calculate the degree to which a certain function can increase user
satisfaction, calculate the percentage of each attribute, and filter the functional requirements.
The satisfaction coefficient of the functional requirements of the digital museum is shown in Figure
1. The Better coefficient of Exhibit Extended Information F2 is the largest at 0.774, followed by
Reservation Consultation, Ticket Purchase Channel F20 and 3D Scanning Browsing, AR Browsing
F14, which are 0.685 and 0.669, respectively.In the result of Worse coefficient, the absolute value of
Liking, Commenting, Collecting F12 is the largest at 0.785, followed by Being Exhibit, Upcoming
Exhibit F6 and 3D Model Exhibit F4, with Worse coefficient absolute values of 0.614 and 0.590.
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
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Figure 1. Satisfaction of functional elements of digital museum
Through the results of the calculation can be obtained to meet the user’s functional requirements of
the elements can be as early as possible on the line, and eliminate the impact on user satisfaction is
not high can be delayed on the line, in practice, but also through the relevant interests of the staff to
conduct research on demand, to meet most of the needs of the priority on the line. According to the
preliminary research, it can be obtained that the functions that can be launched on the line should
best include the charismatic demand and the expected demand, and then according to the actual
situation to make trade-offs.
The prioritization of digital museum functional elements is shown in Table 3, which identifies four
levels of digital museum interaction design functional elements, with the first to the fourth levels
including 8, 3, 8 and 3 elements respectively. Each functional requirement item of digital museum
interaction design uses fuzzy Kano model to calculate the on-line priority, but specific analysis of
specific problems, each case has its own different characteristics, when a function is on-line, it is
necessary to make a comprehensive judgment, and the basic principle is that important and urgent
requirements are prioritized on-line, and in the on-line process, it can be interspersed with
unimportant and urgent requirements. For example, although “Exhibit Basic Information F1” and
“Exhibit Time and Location F8” in the exhibit function and exhibition information are
non-differentiated needs in the questionnaire survey, these two functional factors are the core
functions of the product, and if they are delayed, they will affect the other displays of the exhibits.
If they are delayed, they will affect the other displays of the exhibits and seriously neglect the core
content of the museum exhibition, so even if they are non-differential factors, they should be in the
first level.
Table 3. Recommendations on the priority ranking of the functional elements of the digital
Functional requirements hierarchy
Symbol
Number
First level
F1, F2, F4, F6, F8, F12, F14, F20
8
Second level
F5, F10, F17
3
Third stage
F3, F7, F9, F11, F13, F15, F19, F22
8
Fourth stage
F16, F18, F21
3
5 Digital museum interaction design and evaluation
Based on the user role model and the functional elements of digital museum interaction design
produced in the previous section, this chapter explores the interactive display form of augmented
0.774
0.634 0.667 0.667 0.656 0.585 0.669 0.602 0.685
-0.481 -0.590 -0.474
-0.614
-0.435
-0.785
-0.504 -0.461 -0.477
F2 F4 F5 F6 F10 F12 F14 F17 F20
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
Better and worse value
Symbol
Better Worse
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
11
reality in the digital museum based on the tangible cultural heritage resources of Sanxingdui, and
conducts usability testing to evaluate the design effect.
5.1 Information architecture and interaction prototypes
5.1.1 Information architecture design
This section focuses on the design of digital museum information architecture for Samsung Mound
augmented reality application, and the design of digital museum information architecture is shown
in Figure 2. From the user’s interaction stage, the experience of mobile augmented reality can be
divided into three processes: before visit, during visit and after visit. Before the visit, it provides
users with online ticketing, Sanxingdui Grand Exhibition, digital cultural relics library and other
functions, and users can learn about the Sanxingdui in Guanghan in advance. During the visit, it
provides users with AR guide of venues, AR guide of scenic spots, AR cultural relics model,
treasure hunting card, voice explanation and other forms of augmented reality display and
experience, providing video, voice, pictures and other forms of information supplement for the
actual visit process. At the end of the visit users can share what they see and feel to the community
and establish community connections, as well as view the collected exhibits and acquired treasure
cards in the personal center.
Digital museum
information architecture
Opening page
AR
Home page
Visiting the
Garden
Community
Mine
Digital Archive
Scan
recognition
3D library of
artifacts
AR Sanxingdui
Sanxingdui
Exhibition
Play with
Sanxingdui
Navigation bar
Treasure hunt
Collect
AR navigation
Follow
Featured
Circle
Hot topic
Post statuses
My collection
My attention
My news
Figure 2. Digital museum information architecture design
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
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5.1.2 Interactive page design
The interactive interface is designed on the basis of low-fidelity prototype drawings, adding visual
specification levels, including textual content, color schemes, graphic symbols, labels, tips and
other interface information elements. Users will perceive and prejudge the next system behavior
through these interface elements, and good feedback can make users feel happy. In the design
principles of information products, all interface design is based on user value, and the interface
function design refers to the functional requirements elements of digital museum interaction design
analyzed in the previous section.
5.2 Augmented Reality Module Design
5.2.1 Interactive Artifact Model
The three-dimensional digital model is chosen to be representative of the bronze Daliens, gold
masks and bronze human heads. The first step is the acquisition of 3D digital model, first of all in
the 3D max 3D modeling software for digital model construction, construction is completed after
the material and texture overlay and rendering of the final 3D model, the model will be exported to
the FBX type file, export 3D max need to be in the FBX export panel to check “Embed Media
When exporting to 3D max, you need to check the “Embed Media” option in the FBX export panel,
so as to avoid the loss of textures and maps. Make the model recognition image in Vuforia, the
recognition image requires high accuracy, and graphics with low accuracy cannot be recognized
successfully. Import the recognized image and the model file into Unity Hub together. When
importing, you need to modify the parameters of the imported model, adjust the size and parameters
of the model, overlay the voice and video resources, and build the augmented reality scene. During
the process, you need to debug several times until it is successful.
5.2.2 AR Guided Tour
1) AR Geotagging
Geotagging superimposes virtual tags onto the real world through AR technology and
reflects target location and name information through the size and folding of the tag. It
includes both internal and external environments. In the external environment, users can see
all the geographic information around them. Even if there is occlusion between buildings,
the occluded targets can be presented on the screen for the user to view. In the indoor
environment, users can see the internal structure of the building, such as the parking lot,
ticket office, restrooms, and escape routes. After the user clicks on the label, the target
information will be displayed and the best exhibition route for the user will be shown.
2) AR Walking Navigation
AR navigation is developed on the basis of AR geotagging. When the user uses walking
navigation, the image and current position in the phone will change as the user’s position
moves. When the user reaches the target location, the system will also obtain the user’s
real-time location information through the server and present it, so that the user can
understand where he/she is currently. The system can quickly find the destination in some
complex environments and quickly find the best walking route to the destination. Walking
navigation obtains the current user’s longitude and latitude information through a
localization function and then determines the walking route through a map. The model and
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
13
data are transferred to the server. After the route is built this path will be placed on a smart
AR device through Unity Hub for virtual reality interaction.
5.3 Evaluation of design effectiveness
Usability testing is a testing method in which the designer evaluates the usability of a product by
letting typical users complete the experience of using the design prototype, and by observing,
recording and analyzing the user behavior and related data. Usability testing mainly includes
content testing of the product’s usefulness, interaction flow and user satisfaction. During the testing
process, the smoothness of the user’s operation flow and problems in operation are observed, as
well as the user’s subjective satisfaction level. In the following section, usability testing is
conducted on the designed digital museum form.
5.3.1 Testing process
The usability test was conducted in the form of offline experience, where the pre-designed
high-fidelity interface was made into an interactive prototype, 10 users, including 5 men and 5
women, were selected to ensure the reasonableness of the data, and the users were allowed to
simulate the real online use of the product on their cell phones for the test.
Users were given five main functional tasks (Task 1 ~ Task 5), and at the end of the tasks, with the
help of a standardized user satisfaction scale designed by USE Interactive Interfaces, the testers
were asked to find out their satisfaction with the product, and to find out whether the information
architecture was reasonable, whether the interface information was clear and easy to use, and
whether the product functions were reasonable and useful. The standardized user satisfaction scale
includes four dimensions: usefulness, ease of use, ease of learning, and satisfaction, with four, three,
two, and four questions designed respectively, for a total of 13 questions.
In the testing process, firstly, the design scheme of the digital museum is introduced to the users,
and then the contents to be filled in the questionnaire of USE User Satisfaction Standardized Scale
are introduced, and after the questionnaire is filled in the data of the questionnaire is roughly
browsed, and questions are asked to the users, and the problems in the process of the interface
experience are asked and recorded.
5.3.2 Test results
The results of users’ task completion are shown in Figure 3, which shows that users are basically
able to complete the tasks of the main function. Among them, Task 3 and Task 4 spend a long time,
with an average time of 59.6s and 62.4s. Task 1 and Task 2 spend a relatively short time as a whole,
with an average time of 50s or less.
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
14
Figure 3. User task completion result table
The standardized results of user satisfaction are shown in Figure 4. The average scores of the
questions generally ranged from 6 to 8, and the test users considered that the digital museum
generally satisfied the needs of the interactive experience. From the analysis of the results, the
average scores of usefulness, ease of use, ease of learning and satisfaction of the interaction design
of the digital museum were 7.55, 7.33, 7.30 and 7.00 respectively.
The following conclusions were summarized through the main task tests and the USE User
Satisfaction Criteria Quantitative Scale:
1) In the design of the digital museum information architecture, the functional design is
basically recognized, but there is still some room for improvement for the operation steps
and the learning cost of the users, and the layers of the map need to be raised.
2) After communicating with users about the experience of using each function, most users are
generally satisfied with the design of the main functions of the digital museum. In terms of
the design of the interface, users think that its visual style is in line with the aesthetics, and
the icons and guiding gestures better help users to operate. In terms of the use of the
functions, it improves users’ motivation to participate and their knowledge of cultural relics.
Therefore, for the current design experience, the overall functional design is relatively
reasonable, and the operation steps and jump logic of some functions need to be further
optimized.
Average
User 10
User 9
User 8
User 7
User 6
User 5
User 4
User 3
User 2
User 1
0
20
40
60
80
100
Task 1
Task 2
Task 3
Task 4
Task 5
Time/s
Users
Tasks
Task 1
Task 2
Task 3
Task 4
Task 5
Digital Museum Interaction Design Based on Artificial Intelligence and User Role Modeling
15
Figure 4. Standardized result table of user satisfaction
6 Conclusion
In the era of artificial intelligence, museum construction needs to break the limitations of traditional
museum construction. In this paper, based on the establishment of user role model, the functional
elements of digital museum interaction design are selected, and the functional elements are
analyzed in combination with the fuzzy KANO model. It further carries out the digital museum
interaction design and evaluates its design effect. Using the fuzzy KANO model, the study divides
the functional elements into 10 elements with or without difference attributes, 6 elements with
charm attributes, 3 elements with expectation attributes and 3 elements with reverse attributes, and
summarizes the four hierarchical elements of digital museum interaction design. Combining the
user role model and the hierarchical categorization of functional elements, the interaction design
experiment of the digital museum is carried out with the Samsungdui culture as an example.
Through testing and evaluation, the time spent by users on setting tasks is mostly within 60s, and
they are able to complete the main interactive tasks, and the scores of usefulness, ease of use, ease
of learning, and satisfaction with the interaction design of the digital museum range from 7 to 7.55,
which indicates that users are more satisfied with the interaction effect of the designed digital
museum as a whole, and it is able to satisfy most of the leisure-type, learning-type, and scientific
research-type users’ needs.
Through quantitative research, this paper summarizes the experience needs of users when visiting
the display of exhibits and constructs a user role model, which provides a basis and guidance for the
subsequent design. Based on artificial intelligence technology, the interaction design architecture of
Sanxingdui Digital Museum is proposed, and the design effect evaluation is realized, which
provides a reference case for the digital transformation of the museum.
Funding:
1) The key project of scientific research in universities of Anhui Province: “Research on the
Promotion Path of Wisdom Museum under The Joint Sense Design Thinking - Taking
Anhui Museum as an example” (Project No.: 2023AH051683).
2) Anhui Provincial Quality Engineering Teaching and Research Project: “Exploration of
effective Integration of Ideological and Political Education in Courses under the Background
1
2
3
4
5
6
7
8
9
10
11
12
13 0
2
4
6
8
10
Average value
Item
Average value
Lulu Qu & Zhigang Cheng. Applied Mathematics and Nonlinear Sciences, 10(1) (2025) 1-17
16
of The College of Industry - Taking Visual Communication Design Major as an example”
(Project No.: 2022jyxm483).
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About the Author
Lulu Qu (1988.11-), female, from Nanyang, Henan province, associate professor at Digital Creative
School of Modern Industries, Anhui Sanlian University, main research interests: visual art and
interactive design, regional culture research.
Zhigang Cheng (1989.1-), male, from Wuhan, Hubei Province, senior engineer at Anhui Provincial
Urban and Rural Planning and Design Research Institute Co., Ltd., main research: territorial spatial
planning and urban design.
