In order to solve the problem that the traditional self-organized criticality identification method is weak in dealing with graphical data and nonlinear coupling factors, this paper proposes a self-organized criticality identification method based on SC-GCN network. First, the self-organized criticality evolution process is simulated based on the OPA model. Secondly, the feature quantities, including bus features, line features and global features, are obtained and the data are processed and classified as graph data. Further, a SC-GCN network model is obtained by adding jump connections to the traditional graph convolutional neural network to solve the gradient vanishing problem and accelerate the model convergence. Finally, the dataset is input into the SC-GCN model for training and testing, and the simulation results show that compared with the traditional self-organized criticality identification method, the self-organized criticality identification method proposed in this paper can be more perfect, simple and rapid to identify the self-organized criticality online.