In in-depth research on artificial intelligence technology, especially reinforcement learning in the field of energy optimization scheduling, the training efficiency and information utilization rate of the system state expressed as a vector for the learning mode, is low. In response to this problem, this paper proposes an integrated energy system optimization scheduling method that integrates the graph neural network model and reinforcement learning. First, the electricity-heat-gas integrated energy system is modeled as graph structure data, making full use of the topological information of the system. Second, a reinforcement learning model based on graph neural network architecture is proposed, so that it can make full use of graph structure information to achieve a faster training speed and obtain a larger exploration space. Finally, the graph structure information representing the system state is sent to the model for training, and the example simulation verifies the training efficiency and exploration ability of the method.