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U71Mn high manganese steel is the main raw material of railway track in China. When the milling parameters are not reasonable, it is easy to cause large martensite on the metal surface and work hardening, which is difficult to meet the requirements of use. In order to solve this problem, 1000 sets of cutting data sets were obtained by milling U71Mn high manganese steel with m-v5cn, and a surface roughness prediction model based on xgboost algorithm was established. As a nonlinear model, many of its training parameters were to maximize the performance of xgboost model. An improved hybrid coding DE algorithm was proposed to optimize the model parameters. After the establishment of the model, the maximum error of xgboost decreases by 7.4%, the average absolute error decreases by 11.7%, and the variance decreases by 7.4%. Compared with the mainstream DNN and GA-SVM models, the performance of xgboost model is significantly improved, and it can effectively undertake the task of roughness prediction of U71Mn high manganese steel.