Abstract: By analyzing and researching the massive power big data through machine learning algorithm, we can explore and mine the correlation between power data and the correlation model between data attributes, then analyze the current situation and predict the future, so as to improve the security, economy and stability of the power grid.The collected power data are compared and analyzed by three regression analysis algorithms: Support vector machine regression, Gaussian process regression and regression tree. The regression effect is mainly discussed from five aspects: root mean square error, mean square error, mean absolute error, fitting coefficient and running time.At the same time, the hyperparameter optimization of the three algorithms was carried out to obtain the optimal regression model. Finally, it is proved that the regression tree model based on grid tree optimizer has the best fit degree to the power verification data by comprehensively comparison the five indexes.