Digital Protection and Development of Traditional Cultural Resources in China by Artificial Intelligence

Abstract

Digital protection of traditional culture is shifting from physical archiving to semantic computing. Aiming at the problems of existing data islands and content distortion, this paper puts forward a cooperative mechanism of generative protection and man-machine co-creation which is suitable for local cultural narrative. It relies on the knowledge map to disassemble the semantics of unstructured cultural resources and build a quantitative evaluation model. Combined with topological prior knowledge, the specification of digital twin high-fidelity restoration of cultural relics is formulated. It embeds historical prior knowledge and authenticity penalty factors to avoid cultural deviation caused by algorithm illusion. The national-level drama non-legacy demonstration proves the practicability of high-order graph neural network in analyzing complex semantics, and studies both technical application and humanistic core, providing quantitative technical support for the digital inheritance of traditional culture.