Abstract: Aiming at the problem that general social network community detection algorithm could only detect the community with sole relationship, which couldn't represent the semantics similarity of real social community and couldn't resolve the problem of SSN community detection with multiple semantic topic, we propose an SSN (semantics social network) detection algorithm based on topic comprehensive factor analysis. This algorithm firstly defines the multivariate semantics information as topic, takes multivariate TCF (topic comprehensive factor) as the measurement of topics, and the difference of topic density as polymerization direction, and establishes the initial community structure. Secondly, we establish the cost function with the goal of minimizing the semantics similarity inside the communities and maximizing the semantics similarity between different communities, when some boundary nodes change the community. Thirdly, the SAOP (simulated annealing optimization policy) is established based on the initial community and cost function, which takes the value of cost function as parameter when the boundary nodes change. We could optimize the initial community structure globally and achieve the semantics community detection with multivariate. Finally, the effectiveness of SSN community detection is proved by a serial of simulations.