Accurate load forecasting is beneficial to the stable operation of the power system, improving economy and reliability. In order to improve the short-term power load forecasting accuracy, a short-term load forecasting model based on the improved chimp optimization algorithm to optimize long-short-term memory network is proposed. Because the chimp optimization algorithm is prone to fall into local optimum and has low optimization accuracy, the Circle mapping strategy is used to initialize the population to generate a uniformly distributed chimp population, improve the diversity of the chimp population, and lay the foundation for global optimization; secondly, the introduction of a spiral The position update strategy enables the chimp population to have multiple search paths, expand the search space, and improve the global search ability of the population; then, the Levy flight strategy and the adaptive t mutation strategy are introduced to perform disturbance mutation at the optimal solution position to enhance resistance to local extremes. It can improve the convergence accuracy of the algorithm. Aiming at the problem that parameters such as the number of hidden layer neurons and the learning rate of the LSTM network are difficult to select, ICOA is used to automatically find the optimal parameters for the LSTM network, and an ICOA-LSTM load prediction model is established. Combined with the actual data of a certain area, the prediction analysis is carried out. The results show that compared with the BP, LSTM, PSO-LSTM, and COA-LSTM prediction methods, ICOA-LSTM model has higher short-term power load forecasting accuracy, its forecast mean absolute error is 17.07kW, the root mean square error is 21.80kW, and the mean absolute percentage error is 0.37 %.