As knowledge graphs contain rich item attributes and their associated information, introducing knowledge graphs into recommendation systems can to some extent solve data sparseness and cold start problems. For example, recommendation systems based on propagation utilize the graph structure of knowledge graphs to learn relevant features such as user and item representations. However, the contribution of interactive information between entities and relationships to feature representation is often ignored in propagation, so this paper proposed a knowledge aware recommendation model that fused with interaction information between entities and relationships. Firstly, collaborative information and knowledge correlation were integrated, and heterogeneous propagation methods were used to propagate and expand the representation of users and items. Secondly, in the process of propagation, attention mechanism was used to strengthen the interaction information between entities and relationships, enhance semantic relevance, and ensure the effectiveness of knowledge-based high-level interaction between users and items. Then, a knowledge aware attention mechanism was used to distinguish the importance of entity’s neighbors in each layer, and generate representations of users and items more accurately. Finally, to predict the probability of user interaction with item, multiple representations were combined to obtain the final representation of user and item by an aggregator. To optimize the model, KL divergence loss function was added to align the difference between the prediction distribution and the real distribution of the model. Experimental results on three datasets of Last.FM, Book-Crossing and MovieLens-20M show that the proposed model has a great improvement in CTR prediction performance compared with other baseline models.