Aiming at the selection and optimization of temperature key points in thermal error modeling of different CNC machine tools, a new method was proposed to effectively reduce the temperature measurement points and improve the prediction accuracy of thermal error. The method could effectively classify and select temperature measuring points by establishing the correlation and difference between various temperature signal sequences. On the basis of system clustering, the correlation coefficients between variables and Euclidean distance matrix were weighted and fused to optimize the collinearity between measurement points. Then, grey correlation analysis was used to determine the degree of closeness between measuring points and thermal errors, and the optimal measuring points in various subsets were extracted. A thermal error prediction model based on support vector regression was constructed with the optimized measuring points as input. In order to determine the weight coefficient, the number of clusters and the parameters of support vector regression, the optimal prediction model was obtained by using genetic algorithm for parameter optimization. Finally, the proposed method was validated on several machine tools, and the verification and analysis results of three different machine tools were given. The root mean square error of predictions using the proposed method decreased by 24%, 71% and 62% compared with those using the original measurement points, respectively. The results showed that the proposed method can effectively improve the prediction accuracy of the thermal error model on different machine tools under the premise of greatly reducing the number of sensors. The proposed method has good versatility and the predictive performance of the method is stronger compared to other methods.