In information network, different sources publish facts with different degrees of credibility and accuracy. To predict the truth values of the facts, several fact-finder algorithms are suggested which iteratively compute the trustworthiness of an information source and the accuracy of the facts it provides. However they ignore a great deal of relevant background and contextual information. In this paper, we proposed a novel maximum entropy weighted method to processing trust analysis, allowing us to elegantly incorporate knowledge such as the at tributes of the objects and the implications of the sources. Experiments demonst rate that our algorithm ignificantly improves performance over existing. ©, 2015, Chinese Institute of Electronics. All right reserved.