Collaborative filtering algorithm is the most widely used algorithm in personalized recommendation system. Focusing on the problem of date sparseness and poor scalability, a new clustering recommendation algorithm based on user interest and social trust was proposed. Firstly, according to user rating information, the algorithm divided users into different categories by clustering technology, and set up a user neighbor set based on interest. In order to improve the accuracy of the calculation of interest similarity, the modified cosine formula was used to eliminate the difference of user scoring criteria. Then, the trust mechanism is introduced to measure implicit trust value among users by defining the direct trust calculation method and indirect trust calculation method, converted a social network to a trust network, and set up a user neighbor set based on trust. Finally, this algorithm combined the predictive value of two neighbor sets to generate recommendations for users by weighting method. The simulation experiment was carried out to test the performance on Douban dataset, found suitable value of α and k. Compared with collaborative filtering algorithm based on users and recommendation algorithm based on trust, the Mean Absolute Error (MAE) decreased by 6.7%, precision, recall and F1 increased by 25%,40% and 37%. The proposed algorithm can effectively improve the quality of recommendation system.