Wang Tao

School of Artificial Intelligence, Zibo Polytechnic University

Abstract：

While knowledge graph construction provides the structural backbone for organizing intangible cultural heritage (ICH) information, the effective transmission and creative revitalization of heritage in contemporary society demands a complementary suite of intelligent technologies. This paper examines the application of semantic intelligence—encompassing deep learning-based classification and annotation, large language model-assisted knowledge processing, and multimodal heritage data analysis—to the challenges of ICH transmission and innovation. Building upon the knowledge graph framework presented in the companion paper, this study explores how these technologies can transform heritage data into actionable knowledge, support the discovery of latent cultural patterns, and enable innovative applications across education, cultural tourism, creative industries, and community-based transmission. The paper further considers the policy dimensions shaping this technological landscape, addresses critical concerns regarding model interpretability and cultural appropriateness, and proposes future research directions at the intersection of AI and heritage studies.

Key Words：

Intangible Cultural Heritage; deep learning; semantic annotation; large language models; heritage transmission; creative applications; multimodal analysis