Since massive news articles are generated and posted online,news recommendation has become an important way to alleviate user information overload and achieve personalized news information access,which has been widely used in many news websites and news APPs to improve user experience.Different from the traditional product recommendation,in the scenario of news recommendation,the news articles are generated very quickly,and the semantic meaning of news articles needs to be captured from the original news textual content,which bring huge challenges to the traditional recommendation methods which are based on IDs and features.In addition,users' news reading interests are highly diverse and dynamic,making it difficult to accurately model users.In this paper we will introduce several deep learning based news recommendation algorithms,and explore several future directions of news recommendation.