The traditional collaborative filtering recommendation algorithm does not fully consider the impact of user attributes and item classification on similarity calculation, which results in data sparsity and low recommendation accuracy. This study proposes a collaborative filtering recommendation algorithm based on user attribute clustering and item partitioning. The algorithm fully considers the similarity calculation which has an important impact on recommendation accuracy. Firstly, users are clustered by user identity attributes using clustering algorithm, and then the items are classified. In the similarity calculation, category similarity is added. Considering the number of users scored jointly, comprehensive similarity is calculated by weighted coefficient. Finally, combined with average similarity, the nearest neighbor is synthesized by threshold method. The experimental results show that the proposed algorithm can effectively improve the recommendation accuracy and provide more accurate items for users.