Style transfer method for ceramic patterns in the Liao, Jin, and Yuan dynasties based on VGG16 structure and CycleGAN algorithm

Traditional methods face challenges such as weak feature extraction, content distortion, and unstable training in the style transfer of Liao, Jin, and Yuan ceramic patterns.This study proposes a style transfer model based on an improved VGG16 and a cyclic consistency adversarial network, integrating multi-channel input, spatial attention, and a multi-scale feature pyramid, with a three-scale discriminator and structural similarity constraints.Experiments show that in Liao-to-Jin transfer, style accuracy reached 89.7%, content retention 91.2%, SSIM 0.801, and PSNR 27.9 dB.In Jin-to-Yuan and Yuan-to-Liao transfers, average accuracy and retention were 88.4% and 90.5%, with SSIM 0.792 and PSNR 27.5 dB.The three-scale discriminator improved style fidelity by 6.1%, and expert ratings averaged 8.7-9.1.While the method preserves semantic content and enhances stability for flat painted patterns, performance decreases for engraved designs with strong 3D features (e.g., Yaozhou kiln), with a success rate of 78.3%.