Drug repositioning has been widely applied to explore new therapeutic applications for existing drugs, significantly reducing the transition time from laboratory research to clinical application. However, most existing models rely on static training over complex and large-scale networks, lacking detailed analysis and specificity for individual drug-disease pairs. To address this limitation, we propose the subgraph-aware graph attention network (SAGAN), which constructs subgraphs centered on target drug-disease pairs by extracting their surrounding interactions. Within each extracted subgraph, SAGAN employs an attention mechanism to focus on critical interactions among directly associated nodes within the subgraph, while simultaneously capturing cross-level relational patterns. A hierarchical pooling technique is then applied to aggregate the nodes and edges within the subgraph into more compact representations. Additionally, SAGAN integrates neighborhood features and interaction information from drug and disease similarity networks to enhance the expressive power of subgraph features. Finally, the method predicts drug-disease associations as a graph classification task. On three widely used benchmark datasets, SAGAN demonstrates outstanding performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9648 and an average area under the precision-recall curve (AUPR) of 0.9678, showcasing its robustness in handling sparse and imbalanced data. Furthermore, case studies validate the practical utility of SAGAN in predicting potential effective drugs for Alzheimer’s disease and breast cancer.