Silk textiles represent one of the most challenging categories of artifacts to preserve. Deterioration caused by bacteria and other microorganisms targeting fibroin proteins leads to the gradual loss of the original color and pattern characteristics of silk fabrics over time. Consequently, the imperative to effectively safeguard the patterns on silk textile artifacts is paramount. Taking Shu brocade as an example, inadequate conservation efforts directed towards ancient remnants of Shu brocade have resulted in the gradual disappearance of certain patterns, thereby negatively impacting the lineage of Shu brocade patterns. Historically, strategies for safeguarding ancient patterns can be categorized into traditional and modern methods. Among these, traditional approaches encompass the preservation of fragmentary patterns through techniques such as digital imaging or manual illustration. However, the lack of integration with image preprocessing technologies has led to varying degrees of pattern detail loss during the rendering process, undermining the authenticity of pattern propagation. Modern methods predominantly rely on digital image processing techniques and deep learning algorithms to address issues associated with the damage, blurriness, noise, and low resolution inherent in ancient patterns. These methods also involve the computer-generated production of vector outlines for the patterns. Nevertheless, there is currently a dearth of automated completion algorithms tailored to intricate patterns. Additionally, the utilization of conventional digital image processing techniques falls short in achieving super-resolution reconstruction of degraded images. Limited research has hitherto employed deep learning-based algorithms to realize super-resolution reconstruction of patterns on silk textile artifacts. To address the challenges posed by the current preservation techniques for ancient patterns, including issues of poor applicability to complex patterns and unsatisfactory results in super-resolution reconstruction, we presented a digital preservation approach for ancient Shu brocade patterns using a combination of Generative Adversarial Networks (GANs) and vector drawing techniques. Focusing on the “Brocade with the Pattern of Four Heavenly Kings Hunting Lions” as our research subject, we began by analyzing the foundational structure of the original pattern and extracting cyclic units. We applied a similar region completion strategy to address extensive areas of damage and blurriness within the pattern. Subsequently, an improved GAN was employed to achieve super-resolution reconstruction of the completed image. Following this, vectorization software was employed to create a vector model of the pattern. Finally, a comprehensive blur evaluation method was utilized to assess the effectiveness of the vectorized pattern reconstruction. This study introduced an improved GAN algorithm to the domain of super-resolution reconstruction for textile patterns, utilizing vector reconstruction techniques to achieve high-precision restoration of ancient Shu brocade patterns. Experimental results indicate that, compared to the traditional SRGAN algorithm, the image super-resolution results using our approach exhibit a 0.89 dB increase in PSNR, a 0.008 7 increase in SSIM, and a 0.43 increase in MOS values based on subjective evaluations, aligning with the perceptual experience of the images. The comprehensive blur evaluation results suggest that the membership degree of “poor”, “fair”, “good” and “very good” restoration effect is 1.92%, 13.26%, 47.78% and 36.94%, respectively. According to the principle of maximum membership degree, the restoration effect is categorized as “good”. The patterns restored with our approach demonstrate greater accuracy and richer detail compared to patterns restored with other methods. Furthermore, our method exhibits superior accuracy in reproducing pattern details, further validating its superiority. The digital restoration of ancient Shu brocade patterns presents a novel approach to the preservation and presentation of these artifacts, enabling a diversified dissemination of the artistic and cultural value inherent in ancient Shu brocade. The outcomes of this research are expected to contribute to enhancing the quality of digital restoration for traditional woven brocade patterns. Moreover, these findings offer a valuable technological framework for the digital restoration of patterns found in ancient woven textiles. [ABSTRACT FROM AUTHOR]