A Knowledge-Engineering Approach to the Digital Heritage and Innovation of Traditional Chinese Ink Painting Techniques

Traditional Chinese ink painting (guohua) embodies a profound cultural and aesthetic system rooted in millennia of artistic practice, yet its transmission remains largely tacit and vulnerable to loss in the digital era. This paper presents a knowledge management (KM) framework to preserve, formalize, and revitalize guohua by modeling and managing its core artistic knowledge, including brushwork, ink tonality, and compositional principles such as liubai (blank space) and san yuan fa (three perspectives). Integrating computer vision, deep learning, and interactive interfaces, the multi-stage system quantifies traditional techniques, analyzes spatial composition via semantic modeling, enables multi-style fusion through a multi-style GAN, and supports gesture-driven creation. A culturally grounded aesthetic evaluation mechanism completes the knowledge loop from creation to assessment, transforming tacit expertise into structured digital knowledge and advancing the intelligent inheritance of guohua.