Aiming at the problems of strong randomness, poor stability and unsatisfactory forecasting accuracy of power load, a short-term power load forecasting method combining particle swarm optimization PSO and least squares support vector machine LS-SVM is proposed in this paper.The input factors of the model are load data and meteorological information, particle swarm optimization algorithm is adopted to realize the automatic selection of the parameter of the support vector machine,the least squares support vector machine short-term load forecasting model optimized by particle swarm optimization is established.The accuracy and validity of the improved prediction method are verified by simulation, the results show that the improved method brings benefits to convergence, thinking accuracy and training speed.This study provides a reference for the development of short-term load forecasting methods in China.