In the tasks of graph classification， the graph embedding representations obtained by the existing dropped nodes based graph pooling algorithms do not effectively utilize the implicit information in the dropped nodes and the node information between graphs. Meanwhile， traditional methods do not learn the graph embedding separately， thereby limiting some of its performance in graph classification tasks. To address the shortcomings of traditional methods， a Graph classification method based on graph Pooling Contrast Learning （GPCL） was proposed to effectively utilize the dropped node information. Firstly， the graph attention mechanism was utilized to learn the corresponding attention score for each node， and the nodes were sorted based on the attention scores and the nodes with lower scores were dropped out. Then， the nodes retained in the graph were treated as positive samples， while the dropped nodes from other graphs were treated as negative samples， the embedding representation of the graph was considered as the target node， and the pairwise similarity scores were calculated for contrast learning. Experimental results demonstrate that on D&D （Dobson PD-Doig AJ）， MUTAG， PROTEINS， and IMDB-B datasets， GPCL improves the accuracy in graph classification tasks by 5.79， 15.54， 5.42， and 1.75 percentage points respectively compared to the method using attention mechanism with hierarchical pooling alone. It is verified that GPCL enhances the utilization of inter-graph information effectively and performances well in graph classification tasks.