Aiming at the shortages in trust analysis of existing social recommendation algorithms, a social recommendation algorithm based on multi-dimensional trust and collective matrix factorization was proposed with full use of user trust relationship mined from social auxiliary information. Firstly, the dynamic and static local trust relationships were extracted respectively from social interaction behaviors and social circle features of the user, and the global trust relationship was extracted from the structural features of trust network. Then, a social recommendation algorithm was presented by collective factorizing the enhanced following relationship matrix and the social trust relationship matrix, and a stochastic gradient descent method was utilized to solve the algorithm. The experimental results on the Sina microblog dataset indicate that the proposed algorithm outperforms some popular social recommendation algorithms such as socialMF, LOCABAL, contextMF and TBSVD (Trust Based Singular Value Decomposition), in terms of recommendation accuracy and Top- K performance.