Complex networks exhibit inherent community structures that contain rich topology information of graphs. Existing graph neural networks (GNNs) have not fully utilized community structures and integrating them into GNNs has the potential to enhance nodes' representation capabilities and prediction performance. In this paper, we propose a community-aware graph neural network (CAG), which designs a community subgraph explorer (CSE) that leverages monte carlo tree search (MCTS) to select the most informative subgraphs within communities.