In order to solve the cold-start problem of the traditional collaborative filtering algorithm and to improve the performance of recommendation, this study focuses on the cold-start problem and proposes two algorithms. Cold-start problem of new users:user-based collaborative filtering algorithm integrated with user's information model, cold-start problem of new items:item-based collaborative filtering algorithm applying hierarchical clustering. After a series of experiments carried out on public data sets-MovieLens, comparing the difference between the precision and recall value of the improved algorithm and the traditional one, the results show that the new algorithm can effectively alleviate the cold start problem and improve the quality of recommendation.