Research on Traditional Chinese Painting Style Conversion Based on Computer Vision Technology

As an important carrier of Chinese culture, traditional Chinese painting poses image generation challenges that differ fundamentally from those of Western art styles. This study focuses on traditional Chinese painting style transformation, aiming to investigate how modern computer vision algorithms can effectively extract and reconstruct distinctive stylistic features and enable the transformation of natural images into Chinese painting–style imagery. A style transfer network integrating structural perception and texture constraint mechanisms is designed. Multidimensional evaluation metrics are introduced to validate and compare the effectiveness of the proposed model. The results demonstrate that the method improves overall visual quality while preserving the unique artistic charm of traditional Chinese painting. This study therefore offers a new perspective on the integration of traditional art and artificial intelligence.