A Method for Designing Contemporary Ceramics Informed by Visual Expression Models

With the rapid advancement of artificial intelligence technology, modern ceramic art design is undergoing a digital and intelligent transformation. This paper proposes a design method for modern ceramic works based on a visual expression model that integrates deep learning and cognitive computing technologies to enhance both design efficiency and artistic expression. By incorporating deep feature extraction, attention mechanisms, and the Swin Transformer framework, the detailed representation of ceramic art images is optimized. Additionally, 3D image reconstruction technology is employed to facilitate the generation of ceramic works. Experimental results demonstrate that the proposed method achieves higher accuracy and better performance than existing approaches in ceramic image classification and reconstruction tasks. This research fosters the integration of ceramic art creation with artificial intelligence technology and advances the application of intelligent design.