To solve the problem that graph neural network (GNN) cannot fully load the entire attributed graph due to limited memory resources,the binary identify-aware graph convolutional network (BID-GCN) is propo sed.In this network,the nodes information is considered recursively during message passing, and then in order to obtain an embedding of a given node,the BID-GCN will extract the ego network centered at that node and perform multiple rounds o f heterogeneous message passing,applying different parameters to the central node of the ego net work to the rest of the nodes.In this process,the network parameters and input node features are bin ary by the network.In addition,the original matrix multiplication is modified to be binary to speed up the operation.Through theoretical analysis and experimental evaluation, BID-GCN can reduce the memory consumption by the average approximate 36 times of both the network parameters and input data,and a ccelerate the inference speed by the average approximate 49 times on the citation networks.It can provide comparable performance to full precision baselines,and can better tackl e the problem of limited memory resources.