Traditional collaborative filtering algorithm relies much on ratings among users, which is prone to cold start and data sparsity. In addition, the recommendation results are single. To solve the above problems, this study proposes a diversified movie recommendation algorithm based on trust factor. Firstly, the calculation method of user similarity is improved, and the trust relationship and attribute characteristic information between users are introduced. Next, clustering is conducted to divide users with the same interest into the same community. Finally, user activity, as the movie recommendation degree, is taken into consideration comprehensively in the rating. The penalty factor is introduced, so as to facilitate personalized and diversified movie recommendations for target users. Experimental results show that the proposed algorithm can improve the recommendation accuracy and diversity, achieving a good recommendation effect.