In order to establish a more accurate prediction model for the thermal error of the motorized spindle,a certain motorized spindle was used as the experimental object,the temperature rise and thermal elongation data at 10 000 r/min speed were measured.By using fuzzy clustering and grey relational analysis (FCM-GRA) theory,the temperature measurement points were optimized.By using a combination of whale optimization algorithm (WOA) and support vector regression (SVR),the thermal error prediction model of the motorized spindle was established.The prediction effects of multiple linear regression,SVR and WOA-SVR prediction models were compared.The results show that by using the support vector regression prediction model optimized by the whale algorithm,the thermal error of the motorized spindle can be more effectively predicted,the maximum fitting error is reduced to 3.72 μm,RMSE is reduced to 1.33 μm，which verifies the feasibility of the proposed method.