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Accurate load forecasting can reasonably arrange the start and stop of the unit and reduce the cost of power generation, especially short-term load forecasting is of great significance for power system control, operation and planning. However, the traditional forecasting methods can not reflect the demand of users timely and accurately. Load forecasting using Distributed Support Vector Regression (SVR) in Hadoop environment, at the same time, the self-call SVR (UD-SVR) method based on uniform design is used to optimize the parameters, and the accuracy of the distributed SVR algorithm implemented in this paper is further improved. The algorithm is validated by a real power load data set. The experimental data comes from real electricity consumption data for 424 consecutive days in a prefecture-level city in western China. The results show that the improved algorithm is feasible for short-term power load forecasting. Not only the prediction accuracy is improved on the original basis, but also the calculation speed is greatly improved with the increase of data volume, which reduces the load forecasting time.