Graph neural networks (GNNs) employ a recursive mechanism to continuously gather information from neighboring nodes, drawing considerable attention due to their efficiency and intuitiveness 1-WL-equivalent GNNs lack expressive ability and fail to capture fundamental structural concepts, such as counting elementary structures like cycles or triangles , which are known to be valuable in bioinformatics and cheminformatics. Despite the Graph Transformer enhancing the effectiveness of graph neural networks to some extent, it has not adequately addressed this issue as it still relies on direct information propagation among neighboring nodes. To overcome these challenges, subgraphs are proposed to enhance receptive fields with a higher structural information perception.