The purpose of this paper is to study the help of generative adversarial networks (GAN) for face generation, and to explore whether the network can have an effect on complex face generation. Training an image translation neural network model based on a generative adversarial network with the help of a large number of real human face data sets. Using the CV2-based face tagging algorithm and the HED-based face edge extraction algorithm to obtain input information, and then based on the translation neural network model Developing a face generation system through Tensorflow, Torch and other frameworks to realize the function of generating real faces through sketches or “changing faces” through existing faces. Finally, this model provides training configuration and training information.