The sharp increase of users and items makes the rating data too sparse,which leads to poor performance of traditional recommendation algorithms. The introduction?of social network information alleviates the data sparsity problem faced by traditionalrecommendation systems. However,the existing social recommendation does not take into account the differences in preferences betweenusers and the weak stability of trust propagation when describing the trust relationship between users. Therefore,a social recommendationbased on user influence and preference consistency is proposed. Firstly,combining the rating information and social information,the truststrength between users is described from the direction of preference consistency,and the hidden information is mined to alleviate the user’spreference difference. Secondly,with the social influence of users,a path with the strongest trust propagation stability is found,whichavoids the loss of trust node information in the process of trust propagation. Then,the user’s rating similarity and trust similarity arelinearly weighted to obtain the user’ s neighbors for rating prediction. Finally, the proposed method and the existing socialrecommendation algorithm are comprehensively tested on the Filmtrust and CiaoDVD datasets. It is showed that the proposed method outperforms the existing recommendation algorithms in MAE and RMSE.