Design and Application of Village Landscape Development System for Intangible Cultural Heritage Protection Based on Deep Learning

Abstract

Preserving intangible cultural heritage (ICH) amidst rapid modernization is a pressing challenge. This study presents the development and implementation of a Village Landscape Development System (VLDS) leveraging deep learning technologies to document, analyze, and promote ICH within village landscapes. Integrating convolutional neural networks (CNNs) for image recognition and Long Short-Term Memory (LSTM) networks for natural language processing (NLP), VLDS processes diverse cultural data, including historical archives, ethnographic records, and community interviews. GIS technology is employed to spatially organize and visualize cultural elements, enhancing decision-making in heritage conservation. The system's efficacy was validated through extensive testing, achieving high accuracy (92.5% in image recognition and 88.3% in NLP), with strong precision and recall metrics (image recognition: precision 93.1%, recall 91.8%; NLP: precision 87.5%, recall 89.0%). Community feedback from 150 participants rated VLDS highly in usability (4.7/5), accuracy (4.5/5), and relevance (4.8/5), underscoring its practical impact and acceptance. VLDS exemplifies how advanced technologies can bridge traditional knowledge systems and modern preservation methods, fostering sustainable cultural heritage management. Future research will focus on enhancing the system's adaptability and addressing ethical considerations of data sovereignty and community representation. This study contributes to the evolving discourse on technology-enabled heritage conservation, advocating for inclusive and community-driven approaches.