Drug substructure plays a crucial role in predicting drug-drug interaction (DDI) with combination drugs for disease therapies. In order to exploit the effect of drug substructure on DDI prediction, we propose a Key Substructure-aware Graph Transformer Network for Drug-drug Interaction Prediction (KSGTN-DDI). First, the substructure-adaptive graph Transformer module adaptively explicit encoding of drug structures information. Then, the key substructure-aware module calculates the importance of different substructures in DDI prediction. Finally, the calculated important substructure aggregation features are used to reconstruct the drug-drug interactions. Relevant experiments indicate that the performance of KSGTN-DDI outperforms other DDI prediction models.