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Graph neural networks (GNN), which extend deep neural networks to non-Euclidean data, have been proven to be powerful for numerous graph related tasks such as node classification, link prediction, and graph classification. On the task of graph classification, recent studies aim to learn graph-level representation through a hierarchical pooling procedure using the local and global structure information of the graph. After comparing and analyzing the current graph classification model, this paper proposes the structure and feature fusion pooling model (SAFPool) considering shortcomings of the current method and combining the advantages of different methods. SAFPool utilizes Graphtwo assignment matrix generation modules during the pooling process, which are structure-based cluster learning and feature- based cluster learning modules. Structure- based cluster learning module clusters nodes with similar structures based on graph structure information, and feature- based cluster learning clusters nodes with similar features based on graph node features. Then the two clustering assignment matrices are weighted and aggregated to obtain the clustering assignment matric which implements the clustering strategy to utilize graph structure and node feature information at the same time. Finally, comparative experiments and visualization on multiple graph classification datasets demonstrate the effectiveness of using graph node information and structure information to implement a clustering strategy in graph classification. [ABSTRACT FROM AUTHOR]