As a breakthrough technology in the field of deep learning, Generative Adversarial Network (GANs) enables precise simulation of dataset distributions and the generation of high-quality samples through an adversarial training mechanism involving generators and discriminators. This paper focuses on the innovative application of GANs in the traditional paper-cutting art, unveiling their significant potential to enhance creative efficiency, reduce costs, and facilitate personalized customization. As the traditional paper-cutting art faces challenges from mechanized production and diminishing cultural identity, the application of GANs not only expands the diversity of traditional artistic expressions but also enhances visual appeal and user experience, offering a new pathway for the modern transformation of traditional arts. By deep learning the characteristics of the paper-cutting art, GANs can accurately simulate and innovatively design traditional patterns, providing technical support for the digital preservation and inheritance of intangible cultural heritage. This paper systematically analyzes dataset construction, model training, result evaluation, cultural adaptability, and secondary creation, and explores issues such as the cultural adaptability and authenticity of GAN models in the generative process of the paper-cutting art. With the continuous development of technology, GANs are poised to unleash their innovative potential in a broader range of design fields.