In this paper, we use the graph convolutional network (GCN) for feature aggregation. Our approach, termed as GCN-RL, can directly deploy on a holonomic mobile robot without any tuning. We first use GCN to extract the hidden features among the robot and humans. These extracted features that represent the spatial relationships and agents-agents interactions are then fed into the actor-critic learning framework. Finally, the deep RL network is optimized based on the aggregated features from GCN and the actor-critic framework. The GCN-RL enables a safer and more efficient navigation policy than the other RL navigation methods. The experiment results show that the proposed learning approach significantly outperforms ORCA and other RL navigation methods.