Abstract: In order to improve the accuracy and real-time performance of power user information in the power system, a power user information visualization technology based on GA-BP algorithm optimized K-means algorithm was studied. Using the K-means clustering algorithm, combined with the minimax distance criterion to determine the clustering center, and comprehensively collecting users' personal information, electricity consumption records, electricity expenses, and payment behavior data. By using genetic algorithms to finely adjust the neural network model and automatically optimize the number of hidden layer nodes, the optimal weight and threshold configuration can be obtained, significantly improving the accuracy of user electricity prediction. Subsequently, JFreeChart was used to achieve graphical display of user electricity data, including electricity monitoring, electricity forecast results, and payment activity overview, providing users with an intuitive and easy to understand electricity information visualization interface. The experimental results show that the proposed method can improve the similarity between new clusters and original clusters, achieve accurate statistics and prediction of users' actual electricity consumption, and visually display various monitoring results through the interface, effectively improving the user experience and the level of intelligent power management.