In order to improve the accuracy of power load forecasting and deal with the challenge of insufficient stand-alone computing resources brought by the intelligent power system, data extraction algorithms are used in energy market analysis. Preliminary weather performance algorithms are optimized online based on the nature of the power load data. In order to improve the accuracy of the computational algorithms, the concept of classification and various agents was introduced. The MapReduce cloud computing programming framework is used simultaneously to improve design algorithms to improve the ability to process large amounts of data. The actual electronic data provided by EUNITE was selected as a sample analysis and a complete experiment of the 32-node cloud computing group. The results of the experiment show that the load data provided by EUNITE was expanded into four different data sets: 1000 times, 2000 times, 4000 times, and 8000 times. Works on older data and the cloud. Platforms with groups of 4, 8, 16, and 32 nodes are designed to calculate acceleration ratios and scale speeds. The acceleration ratio of a perfectly parallel system algorithm can reach 1. However, in practical applications, as the number of cluster nodes increases, so does the transmission cost of the node network. Conclusions. Accuracy assumptions based on this model are better than the general evaluation of supported vector regression prediction algorithms and neural network algorithms, and the planning process is well underway.