Digital Reconstruction and Cross-Cultural Knowledge Service of Lingnan Garden Heritage

Abstract

In this study, the author focused on the digital preservation and cross-cultural communication of Guangzhou Thirteen-Line Garden, a typical heritage site of the Maritime Silk Road. A dual-cycle Building Information Modeling-Convolutional Neural Network fusion framework was constructed to maintain dynamic consistency between geometric accuracy and texture semantics in garden reconstruction. The Multi-View Context Semantic Refinement algorithm was proposed to enhance texture recognition performance for complex components and weathered surfaces in Lingnan garden environments. A Web Extended Reality-knowledge graph multilingual interactive platform was built to support dynamic knowledge organization, real-time semantic reasoning, and cross-cultural narrative delivery. Experimental results demonstrated that the integrated framework significantly improves geometric-texture alignment, reduces system rendering delay, and effectively extends user engagement and learning experience. This approach unifies high-fidelity modeling, semantic knowledge reasoning, and public cultural services.