Jincang embroidery, a traditional skill in Quanzhou, Fujian, China, has Litchi Jump, Jincang Convex embroidery, Dragon Scales Overlapping Armor, and gilt embroidery stitches as its main forms of artistic expression. These embroideries are unique in style and visually exquisite, but the technological process is complex, time-consuming, and needs to be done by hand existing research on the transfer of embroidery styles is relatively scarce and does not cover the unique artistic expression of Jincang embroidery. In view of this, a convolutional neural network algorithm suitable for the transfer of Jincang embroidery styles is proposed. First, the convolutional neural network (CNN) VGG-19 style transfer algorithm model was utilized, regarding the Jincang embroidery texture image as a style image. Second, image style features are captured by constructing a Gram matrix. Then the L-BFGS algorithm is applied for iterative optimization to finally generate images with the texture image style of Jincang embroidery. The quality of the migration results was evaluated by PSNR and SSIM. The results show that the images of irregular dot patterns and hand-painted flower patterns fused with Jincang embroidery are of higher quality. Therefore, applying computer vision technology can innovate Jincang embroidery design, so that the surface texture characteristics of Jincang embroidery are transferred to the design of textile fabric patterns, not only for the Jincang embroidery to inject new vitality but also for the creation of textile fabric pattern design to provide a new way of thinking.