To address the problem that existing knowledge graph-based recommendation models only perform feature extraction from one end of users or items, missing the feature extraction from the other end, a bipartite knowledge-aware graph convolution recommendation model based on knowledge graph is proposed. First, the initial feature representation is obtained by random initialization characterization of users, items and entities in the knowledge graph; then, a user and item-based knowledge-aware attention mechanism is used to simultaneously extract features from both users and items in the knowledge graph; next, a graph convolutional network is used to aggregate feature information in the knowledge graph propagation process using different aggregation methods and predict the click-through rate; finally, the effectiveness of the model is verified by comparing it with four baseline models on two publicly available datasets, Last.FM and Book-Crossing. On the Last.FM dataset, AUC and F1 improve by 4.4% and 3.8% respectively, and ACC improves by 1.1%, compared with the optimal baseline model. On the Book-Crossing dataset, AUC and F1 improve by 1.5% and 2.2% respectively, and ACC improves by 1.4% . The experimental results show that the model in this study has better robustness than other baseline models in AUC, F1 and ACC metrics.