Collaborative filtering algorithm is the most widely used recommendation technology in e-commerce system. In order to alleviate the shortcomings of traditional user-based collaborative filtering algorithm in cold start, recommendation accuracy, and data sparsity, this study proposes collaborative filtering recommendation algorithm based on user characteristics. This algorithm extracts the attribute features by using the registration information, extracts the interest features and trust degree from the existing scoring information, and synthesizes the feature similarity of the above features to further generate recommendations. Experimental results show that comparing with the traditional user-based collaborative filtering algorithm, the collaborative filtering algorithm based on user characteristics greatly improves the accuracy of the recommendation.