A multi-agent graph convolutional reinforcement learning based on hybrid dilated attention was proposed to address the challenges of capturing diverse dynamic information and computational complexity in complex high-dynamic multi-agent reinforcement learning. The multi-agent graph convolutional reinforcement learning with ensemble-based hybrid dilated attention was proposed to balance computational complexity and receptive field size in order to enhance learning efficiency. The dynamics matrix of the underlying graph was obtained to capture the dynamic interactions of multiple agents. The hybrid dilated attention mechanism used the Bagging method to combine the advantages of the multi-head attention and the multi-scale dilated attention mechanisms in order to enable the model to effectively learn different types of features. A regularization was added into the loss function to enhance the stability of the model and address the overfitting problem. The improved temporally extended greedy method was utilized to choose the multi-agent’s actions in order to achieve the exploration and exploitation trade-off of actions. The simulation results demonstrate that the proposed method achieves higher accuracy and greater stability in policy optimization than the existing approaches in complex dynamic environments.