Value-Added Research on Village Cultural Heritage Experience Supported by Intelligent Information

Abstract

This research presents a framework that enhances village cultural heritage preservation through advanced computational methods. By integrating high-resolution imaging, 3D scanning, and machine learning, the system captures detailed artifact representations and encodes metadata using transformer-based models for contextual analysis. A multi-modal fusion network is employed to dynamically integrate diverse data types, supporting artifact restoration, classification, and anomaly detection. To preserve historical integrity, domain-specific preprocessing ensures semantic consistency with expert knowledge. The system is designed to be adaptive and scalable, accommodating various cultural heritage data and integrating with emerging technologies. Experimental results show the framework’s effectiveness in artifact analysis and highlight its potential for immersive, interactive experiences, offering a sustainable approach to preserving and engaging with cultural heritage.