The existing news recommendation system fails to sufficiently consider the semantic information of news, and modeling factors for news body suffers from unity problems. Attention-BodyTitleEvent (Attention-BTE), a news recommendation algorithm based on fusion of attention and multi-perspectives, is proposed in this study. The BERT model and attention mechanism are applied to vectorize the body, title, and event in the news respectively. The three parts are combined to represent news vectorization, and then the candidate news and user browsing news data are processed respectively to obtain the corresponding candidate news vectorization and user vectorization. Finally, dot multiplication is conducted to obtain the probability of users clicking on the candidate news, namely the news recommendation result. Experimental data demonstrate that Attention-BTE improves the index by about 6% compared with the other news recommendation algorithm.