Abstract: A multi-objective optimization method for process parameters based on neural network and NSGA-II algorithm is proposed to address the problem of low surface processing quality in the milling process of nickel based high-temperature alloy materials.First，different process parameters are employed for CNC milling of nickel based high-temperature alloy Inconel718 and a dataset is obtained.The surface roughness is used as the output and different process parameter combinations as the input.The sparrow search algorithm is employed to establish an SSA-BP neural network model for predicting the surface roughness of Inconel718 during milling；Subsequently，with the maximum material removal rate and minimum surface roughness as optimization objectives，a multi-objective optimization main model for NSGA II process parameters is built.The constructed prediction network model is called the objective function of the main model and optimized to obtain the Pareto optimal solution set.TOPSIS method is employed to make optimal solution decisions on the Pareto optimal solution set and obtain the optimal combination of process parameters.Our optimization results indicate this method can be used for predicting surface roughness in CNC milling of high-temperature alloy materials and optimizing process parameters，further improving the processing quality and efficiency of CNC milling materials.