Establishing a surface quality prediction model for difficult-to-machine materials in automated production is the basis for sustainable manufacturing. A prediction model for processed surface roughness was proposed combining quantum genetic algorithmand support vector regression (QGA-SVR), by which the problem was improved that the existing optimization method was easy to fall into local optimal solution when the model parameters of the search support vector regression were searched. The intersection and mutation operations were added in the process of quantum gate updating to ensure the global search ability of the model. In order to improve the generalization ability of support vector regression, the K-folding crossvalidation was combined in the parameter optimization process. The support vector regression models based on quantum genetic algorithm (QGA) and genetic algorithm (GA)were compared and analyzed by combining the dry turning 304 stainless steel test data and the existing milling experiment data.The results show that QGA-SVR has the advantages of fast convergence and high prediction accuracy. Based on the established QGA-SVR model, the influence law of cutting parameters on the turning surface roughness was analyzed