【摘要】 随着深度学习的迅猛发展，风格迁移技术在算法和应用上取得了重大突破，为内容与风格的创新交互提供了强大支持。该文综述了风格迁移的基本概念、分类及其在神经网络中的应用，特别是神经网络风格迁移的原理、变体与合成算法。文章还对基于文本的图像风格迁移与基于图像的方法进行了比较，分析了各自的优缺点，突显了智能化风格迁移技术的发展。此外，探讨了风格迁移技术与其他领域结合的情况，如与超分辨方法和对比学习方法等的结合，以及在大型工艺品设计中的应用实例，展示了其广泛的应用潜力。该文的目的是为研究者提供清晰的视角，推动风格迁移领域的技术进步。更多还原

【Abstract】 With the rapid development of deep learning, style transfer technology has made significant breakthroughs in algorithms and applications, providing strong support for innovative interaction between content and styles. This paper reviews the basic concepts, classifications and applications of style transfer in neural networks, focusing on the principles, variations and synthesis algorithms of neural style transfer. A comparison between text-based image style transfer and image-based methods was conducted, analyzed their respective advantages and disadvantages, highlighted the development of intelligent style transfer technology. Furthermore, the integration of style transfer technology with other fields was discussed, such as its combinations with super-resolution methods and contrastive learning methods, as well as its application examples in large-scale artwork design, demonstrated its extensive potential applications. The purpose of this paper was providing researchers with a clear perspective and promoting technological advancement in the field of style transfer.更多还原