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 sourceand 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 attributes of the objects and the implications of the sources.Experiments demonstrate that our algorithmsignificantly improves performance over existing.