AI-Powered Pattern Recognition in Art History: A Deep Neural Network Study of Tuanhua Motif Evolution

This study systematically tracks the evolution of Tuanhua patterns from the Neolithic to the Qing Dynasty, covering ten key periods in Chinese art history. By integrating 3,000 HD pattern samples from 28 international cultural institutions, an interdisciplinary analysis model is constructed using digital image processing and deep learning technology. Combined with vector feature extraction, the residual network model is used to perform automatic shape feature recognition and quantitative time difference analysis. Data analysis showed that the X-ray symmetry of the group pattern decreased significantly and the edge complexity continued to increase, revealing the historical trajectory of its function from religious symbol to decorative aesthetics. Combined with a comparison of kiln industry records and cross-cultural artifacts, the study demonstrated that the Eastern transmission of Buddhism, Neo-Confucianism, and other technical factors shaped the pattern's evolution. This research path integrating quantitative analysis and cultural interpretation provides a new methodological framework for interpreting the social dynamics underlying the evolution of decorative arts.