In order to reduce the negative impact of excessive grinding temperature on the thermal damage of parts, and to improve the yield and quality of parts, this study establishes a surface grinding temperature prediction model based on convolutional neural network. Firstly, the temperature data is obtained through finite element simulation, and pre-processing is performed. Then, the convolutional neural network program is written by Google's open-end learning tool TensorFlow, and finally the prediction result is obtained and compared with the simulation value. The results show that the grinding temperature prediction model based on convolutional neural network has strong learning ability and nonlinear fitting ability, which greatly improves the prediction accuracy of grinding temperature.