As the power grid continues to expand and grow more complex each year, the frequency of power grid dispatching operations is increasing. Any misoperation within this process can have serious repercussions. To address these challenges, this study proposes the development of an artificial intelligence model designed to prevent misoperations in power grid dispatching and operation classification. First, we develop a misoperation prevention model based on the combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. This CNN-LSTM-based misoperation prevention model for power grid dispatching is designed to explore the deep and temporal characteristics of power grid dispatching operations, achieve more accurate identification of misoperations, and improve the efficiency of power grid dispatching misoperation prevention. Additionally, we propose a refined misoperation classification method that combines the C4.5 decision tree algorithm with Boolean and Bayesian formulas. This approach aims to achieve a more precise classification of misoperations. The simulation results reveal that the proposed algorithm improves the success rate of misoperation prevention by 2.50% to 3.29%, respectively, compared with existing algorithms.