Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency.