The user-based collaborative filtering recommendation algorithm is based on the calculation of the similarity between users when the neighbor user is screened, and the increase in the amount of data exacerbates the sparseness of the data, which leads to the poor accuracy of the results and affects the recommendation accuracy. Aiming at this problem, this study proposes a recommendation algorithm based on the combined similarity of users. The calculation of combined similarity of users is divided into two parts:the similarity of the user's preference for item attributes and the similarity of the demographic information between the users. The algorithm introduces the LDA model to calculate the preference for the user's item attribute, and the scoring data is only used as the screening basis when calculating so as to avoid using it directly as well as slow down the influence of sparse data on similarity calculation results. While the similarity between demographic information is measured by Hamming distance after the numerlization of demographic information. Experimental results show that the proposed algorithm is superior to the traditional collaborative filtering recommendation algorithm in recommendation accuracy.