To solve poor diversity problem of user interests in content-based news recommendation and cold-start problem in hybrid recommendation, a new method of news recommendation based on fusion of content-based recommendation and collaborative filtering was proposed. Firstly, the content-based method was used to find the user's interest. Secondly, similar user group of the target user was found out by using hybrid similarity pattern which contains content similarity and behavior similarity, and the user's potential interest was found by predicting the user's interest in feature words. Next, the user interest model with characteristics of personalization and diversity was obtained by fusing user's existed interest and potential interest. Lastly, the recommendation list was output after calculating the similarity of candidate news and fusion model. The experimental results show that, compared with the content-based recommendation methods, the proposed method obviously increases F-measure and Diversity; and it has equivalent performance with hybrid recommendation method, however it does not need time to accumulate enough user clicks of candidate news and has no cold start problem.