For processing graph data, a variety of graph neural network approaches have been developed; however, most research focuses on the convolutional layer for feature aggregation rather than the pooling layer for downsampling. Additionally, the computation of assignment matrices is required for the pooling approach to creating clusters, and the pooling method for node scores simply employs one scoring strategy. A new multi-dimension graph pooling operator, MDPool, is presented to solve these issues and increase the precision of graph classification tasks. To calculate node scores in various dimensions, the model makes use of information on node features and graph structure. The score weighting across several dimensions is summarized by using an attention technique to provide more reliable node rankings. The set of nodes is chosen to produce induced subgraphs based on the node rankings. The proposed MDPool can be implemented into a variety of graph neural network architectures. The encode-decode model, EDMDPool, is created by stacking the MDPool pooling operator with the convolutional layer of the graph neural network. In the graph classification tasks of four public datasets, EDMDPool performs better than the existing baseline model.