To solve the accuracy problems of the existing algorithms in calculating the influence of social network nodes

by integrating different dimension information of nodes

and considering the topic distribution vector of nodes on multiple communities

a new model is proposed. It first regards the correlation between topics as the prior information

then uses the mixed membership stochastic block (MMSB) model to express the interaction among nodes

learns topic contents using topic model

and finally

iteratively calculates the global influence of nodes with global topological relationship. We select data from social networks

use P@N

MAP

etc.

as the evaluation indicators

and compare the proposed algorithm with the existing mainstream algorithms. The experimental results show that our algorithm significantly improves the identification accuracy of influential nodes and the validity of ranking.