[Objective]Making up for the shortcomings of existing news recommendations in content information using and long and short-term user interests exploring by considering the current concern and stable preference of users on the basis of making full use of the textual and additional information of news.

[Methods]A news representation model that integrates textual information such as title and abstract, as well as additional information such as explicit and potential topics. A user representation model that characterizes the long and short-term user interests by exploring the user's current concern and stable preference.

[Results] Under four evaluation indices, our proposed model scores 69.51%, 34.09%, 37.25%, 43.00% and 66.05%, 30.93%, 34.30%, 40.46% respectively on two large-scale news recommendation datasets, which is higher than seven advanced baseline models.

[Limitations]We don't give enough consideration to users with few historical behaviors, so the following research will focus on the cold-start users.

[Conclusions]We got informative news and user representation vectors using advanced natural language processing technics. And the designing of our proposed model can improve the performance of news recommendation effectively.