As one of the hot topics in the field of new energy forecasting,it is necessary for the research of short-term wind power forecasting to pay attention to the engineering application of the model while improving forecasting accuracy. Hence,a combined XGBoost forecasting model based on partial maximum information coefficient is proposed. To begin with,a feature selection algorithm based on partial maximum information coefficient is designed. By introducing partial mutual information,while mining meteorological features that have a greater impact on wind power,it can also eliminate the adverse effects of coupled information. On this basis,in order to take the accuracy and computational efficiency of the model into account and reduce the forecasting risk of a single model,a combined forecasting model with XGBoost as the underlying algorithm is constructed to further realize wind power forecasting. Two wind farms with large differences are used as examples for verification analysis. The results show that the combined XGBoost forecasting model based on partial maximum information coefficient feature selection algorithm can not only improve the forecasting accuracy of short-term wind power,but also has higher calculation efficiency compared with similar combined forecasting models,which is beneficial to engineering application.