Abstract: Research on drug-drug interaction (DDI) is conducive to clinical medication and new drug development. Existing research technologies do not fully consider the topological structure of drug entities and other entities such as drugs, targets, and genes in the drug knowledge graph, as well as the semantic importance of different relationships between entities. To solve these problems, this paper proposes a model based on neighborhood relation-aware graph neural network (NRAGNN) to predict DDI. Firstly, the graph attention network is utilized to learn the weights and feature representations of diffe- rent relationship edges, which enhances the semantic features of drug entities. Secondly, neighborhood representations for different layers around the drug entity are generated to capture the topological structure features of drug entities. Finally, the drug-drug interaction score is obtained by element-wise multiplication of the two drug feature representation vectors. Experimental results show that the proposed NRAGNN model achieves 0.899 4, 0.944 4, 0.956 7, and 0.902 3 in ACC, AUPR, AUC-ROC, and F1 indicators on the KEGG-DRUG dataset, respectively, outperforming other current models.