Abstract: Network BBS has been a very important way for Web user to publish information. The discussion on hot topics of BBS affects people's view, attitude to physical world, and even enactment. So a series of problems are put forward: How to compute the network user's influence? What is the user's influence trend? How to compare user's influence in different topics? By the power law, a lot of users are the “long tail”. How to identify the influential users? Taking theme as unit, extracting the reply information among users, we build user cascade relation graph, in which the number of reply and the length of reply make up user's behavior features. In-degree and out-degree make up structural features. Based on the Pagerank algorithm, introducing user's behavior feature and the user relational networks, this paper designs a multiple attributes rank (MAR) algorithm. Influential user's evolution trend is analyzed by time span division, based on user's interest difference on topic at different time. To illustrate these problems, we have conducted experiments with data from Tianya BBS, and evaluated multi-facets of issues of identifying influential users and MAR algorithm. We have summarized some interesting findings and expec ted the future work.