Abstract: In the news recommendation scenario, the traditional text-based feature recommendation model only considers the co-occurrence relationship of words, and cannot capture the implicit meaning and associated knowledge of words.The recommendation model based on deep learning only considers the information of the entity in the process of merging the knowledge graph information, ignoring the connection between the distant entities, resulting in the lack of related information and deep semantic relations between entities.A model named deep knowledge-enhanced network (DKEN) was proposed to solve the problem.Firstly, a long-short-term memory network was used to extract the entity path features from the knowledge graph.And then, path features were added to the attention network and the user feature was built dynamically based on the candidate news.Finally, some experiments were carried out.The results show that the entity path features can improve the model's effect and increase by about 1% on the F₁ indicator.