With the rapid development and popularization of social network site, how to achieve efficient friend recommendation has become a hot issue. Currently, Matrix Factorization algorithm is widely used method by industry. Although the traditional Matrix Factorization algorithm could bring a good results, but there are still some problems. First, this model does not take full advantage of structural relationship between users in social network; Secondly, this algorithm is dependent on the user-rating matrix, which only has secondary scoring and cannot fully express the user's preferences. In order to solve these two problems, a Matrix Factorization model with social network regularization was proposed in this paper, modeling use of social network users in the model the relationship between neighbors. And as an auxiliary information fusion to the matrix Decomposition Model. This?model?can?solve?the?problems?that?traditional?Matrix Factorization model cannot?solve. Though the contrast experiments on tencent weibo data set, verify that our proposed method could obtain a higher mean average precision than other traditional methods.