In order to improve the yield of solid rocket motor combustion chamber shell, a prediction model of spinning data was proposed. Through the analysis and feature extraction of the spinning processing data set, the prediction model is established by using machine learning, and the five-fold cross-validation evaluation model can effectively predict the middle runout and Q runout. The prediction results show that the model provides a basis for optimizing the spinning process and improving the yield of combustion chamber shell.