Aiming at the problem of tail energy consumption and application object limitation in APP energy bug model in traditional research, a system call based energy bug model is constructed. First, mix-cross-recursive feature elimination method is used to select important system calls that affect the energy consumption of each category of APP as features to improve the feature refinement granularity. Then, multiple regression models are constructed for each category of APP. By comparing the average absolute error and coefficient of determination of different models, linear kernel support vector machine regression is selected as the energy bug model of the classified APP. Finally, based on the test set, the average absolute error of the model constructed by the mix cross recursive feature elimination method and the cross recursive feature elimination method is compared. The result shows that the accuracy of the model constructed by the mix recursive feature elimination method is increased by up to 4.4%. At the same time, the classification model is compared with the classification model based on the test set. The average absolute error of the unclassified model shows that the accuracy of the classification model is increased by up to 6.7%, and the classification model can accurately detect the energy bug of the historical version of the APP.