The working state of the pitch motor has a great influence on the operation of the wind turbine. In this paper, the 2MW wind turbine is taken as the research object. Based on the historical operation data of the wind turbine, the influencing factors of the pitch motor temperature deviating from the normal range are analyzed. First, the range of the pitch motor temperature is counted by the quartile method. Then, using Relief-F for feature selection, the characteristic parameters that have a great influence on the pitch motor temperature are screened out. According to the selected characteristic parameters, combined with the historical operation data of the wind turbine, the influencing factors of the abnormal temperature of the pitch motor are analyzed. Through analysis, it is found that the blade pitch angle, the pitch motor current and the battery box temperature show obvious trend changes in the period before and after the abnormality of the pitch motor temperature. They are related factors that affect the abnormal of the pitch motor temperature. The main meaning of this paper is to screen the characteristic parameters that affect the pitch motor temperature through the Relief-F algorithm. The selected characteristic parameters are representative and can be used as the input parameters of the prediction model of the pitch motor temperature. It can better deal with the difficult problem of parameter selection for early warning model modeling.