Objective To improve the efficiency and quality through the neural networks optimization of multiple ion nitriding process on stainless steel. Methods The influences of cycle, ethanol, CS₂ and ammonia flow on the wear resistance and corrosion resistance of austenitic stainless steel 316Ti in nitriding were analyzed. A BP neural network was established, and parameters for the stainless steel circular multiple ion nitriding process were seeked through prediction. Results The process parameters error was optimized within 依5% by using BP neural network to validate the technical parameters of the process optimization. Conclusion Different from the traditional empirical method and test method, the new method realized the optimization design of multiple ion nitriding.