ph adjacency matrix. Then the graph attention network is used to gather the information of the high dimensional time sequence features of the user load according to the dynamic graph adjacency matrix. Finally, the predicted user load is output through the fully connected layer. To verify the validity of the AGAT model, the UCI power load dataset is used for prediction experiments. The experimental results show that the prediction indices of the proposed model are better than those of various baseline models. This is helpful for the improvement of the accuracy of short-term user load prediction.