Abstract: Abstract： At present， most of the recommendation methods based on knowledge graph use single user or item representation， which has the problems of user interest interference， incomplete use of information and sparse data. This paper proposes a multi-view knowledge-aware recommendation model （MVKA）. Firstly， the model captures the user’s interest representation in the user-item graph fusion attention mechanism. Introduce the project-entity diagram， the graph attention network is used for feature extraction to obtain the embedded representation of the item. Then， a comparative learning method of graph perspective is constructed between the two views. Finally， summation and concatenation operations are carried out to get the final representation of the user and the project， and the matching score of the user to the project is predicted by the inner product. In order to verify the accuracy and computational efficiency of the experiment， a large number of experiments were carried out on the three public datasets of MovieLens-1M， Book-crossing and Last FM， and compared with other traditional methods and graph neural network models， the AUC and F1 value evaluation indicators were significantly improved， indicating that the MVKA model can significantly use various information relationship data to improve the knowledge perception recommendation task.

Key words: Key words： knowledge perception recommendation system, attention mechanism, graph attention network, contrastive learning