In order to improve the milling surface quality of screw rotor and other parts with spiral surface. According to the machining characteristics of screw rotor, the single factor rotation milling experiment is carried out according to the spindle speed, feed rate and intermittent feed. The improved particle swarm optimization algorithm is used to determine the optimal value of the initial weight and threshold of BP neural network. The trained improved BP neural network algorithm is used to predict the surface roughness of the milled screw rotor, and compared with the traditional BP neural network. The results show that the training accuracy of traditional BP neural network for surface roughness is the lowest, and the average relative error of 2000 iterations of particle swarm optimization in the improved algorithm is the lowest, which is 1. 21%. Using the model to predict the influence law of process parameters on surface roughness, it can be seen that under the premise of other process parameters unchanged, the surface roughness shows a decreasing trend with the increase of spindle speed; With the increase of intermittent feed rate, the surface roughness first decreases then increases; With the increase of feed rate, the surface roughness decreases first then increases. Conclusion: The improved neural network algorithm can accurately predict the surface roughness of spiral surface after milling, and provide theoretical guidance for the selection of process parameters in screw rotor milling.