Abstract: There are many mechanical components in the nacelle of wind turbine,and taking the nacelle temperature as the research object can realize the early warning of wind turbine failure. Firstly,this paper extracts the nacelle temperature data under the normal operation of the wind turbine,integrates Pearson correlation coefficient and Spearman correlation coefficient,as well as the importance of the characteristic variables of light gradient boosting machine（LightGBM） and CatBoost algorithm,and selects 20 characteristic variables that have a greater correlation with the nacelle temperature as the characteristic variable set of the nacelle temperature of the wind turbine. Then three algorithms,namely CatBoost,LightGBM and Random Forest,are selected to establish models respectively,and mean squared error（MSE）,root mean square error（RMSE）,mean absolute error（MAE） and decision coefficient R2 are used as evaluation indicators for comprehensive evaluation. Finally,the model established by CatBoost algorithm with the best evaluation indicators is selected as the early warning model of abnormal temperature in wind turbine nacelle. The warning effect of the model is verified by using the actual historical data of abnormal temperature in the wind turbine nacelle. This model can give an early warning when the deviation between the predicted value of nacelle temperature and the real value is large. Professional maintenance personnel can prioritize the maintenance of components with high relevance according to the importance ranking of the characteristic variables output from the model,which is more practical.