  Aiming at two molecular graph datasets and one protein association subgraph
dataset in OGB graph classification task, we design a graph neural network
framework for graph classification task by introducing PAS(Pooling Architecture
Search). At the same time, we improve it based on the GNN topology design
method F2GNN to further design the feature selection and fusion strategies, so
as to further improve the performance of the model in the graph property
prediction task while overcoming the over smoothing problem of deep GNN
training. Finally, a performance breakthrough is achieved on these three
datasets, which is significantly better than other methods with fixed aggregate
function. It is proved that the NAS method has high generalization ability for
multiple tasks and the advantage of our method in processing graph property
prediction tasks.
