Aiming at the difficulty in manually designing portrait paper-cuts, this study employs the generative adversarial network (GAN) to generate high-quality portrait paper-cuts for the first time. Based on the artistic characteristics of portrait paper-cuts, an improved network based on CycleGAN is proposed. 1) The CBAM attention module is introduced into the CycleGAN generator to enhance the feature extraction of the network. 2) The local discriminator for key facial regions such as nose, eyes, and lips is introduced to improve the generation effect of the above areas in generated portrait paper-cuts. 3) A new loss function is designed based on image edge information and SSIM, which will be adopted to replace the original forward cycle-consistency loss of CycleGAN and eliminate the shadows in the portrait paper-cuts. Compared with other automatic generation methods of portrait paper-cuts, the proposed method can quickly generate paper-cuts featuring high similarity to the original human face, continuous and smooth lines, and aesthetic beauty. Additionally, this study also puts forward a post-processing method of portrait paper-cut connectivity to make the obtained results more consistent with the overall connectivity of traditional Chinese paper-cuts.