Although traditional collaborative filtering recommendation algorithm can easily find potential users' interests, it remains cold-start problem and sparsity problem. In order to solve these problems, a new hybrid recommendation algorithm is proposed. Firstly, this study builds topic distribution matrix through the LDA topic model, and user interest matrix is created using topic distribution matrix. Secondly, the user interest model is obtained by combining user's historical behavior information and user's content information. Finally, the TOP-N recommendation list is output after calculating the similarity of user and candidate movies. Experiments on the Douban Movies dataset reveals that the results obtained from improved recommendation algorithm are obviously better than that from traditional recommendation algorithm, and it can better deal with sparse data and cold-start problems.