Knowledge graph (KG), as the side information, is widely utilized to learn the semantic representations of item/user for recommendation system. The traditional recommendation algorithms usually just depend on user-item interactions, but ignore the inherent web information describing the item/user, which could be formulated by the knowledge graph embedding (KGE) methods to significantly improve applications performance. In this paper, we propose a knowledge-aware-based recommendation algorithm to capture the local and global representation learning from heterogeneous information. Specifically, the local model and global model can naturally depict the inner patterns in the content-based heterogeneous information and interactive behaviors among the users and items. Based on the method that local and global representations are learned jointly by graph convolutional networks with attention mechanism, the final recommendation probability is calculated by a fully-connected neural network. Extensive experiments are conducted on two real-world datasets to verify the proposed algorithms validation. The evaluation results indicate that the proposed algorithm surpasses state-of-arts by $10.0%$, $5.1%$, $2.5%$ and $1.8%$ in metrics of MAE, RMSE, AUC and F1-score at least, respectively. The significant improvements reveal the capacity of our proposal to recommend user/item effectively.
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