Protection of Guizhou Miao Batik Culture Based on Knowledge Graph and Deep Learning

TL;DR

This paper presents an AI-based approach combining knowledge graphs and deep learning to digitally preserve and understand Guizhou Miao batik culture, enhancing cultural protection and research.

Contribution

It introduces a dual-channel mechanism integrating semantic and visual data, including a knowledge graph and an improved ResNet34 model for batik pattern classification.

Findings

Knowledge graph enables effective cultural information retrieval.

Improved ResNet34 achieves over 99% accuracy in pattern classification.

The approach demonstrates AI's potential in cultural heritage protection.

Abstract

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their subjects can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 99.0%, 99.0%, 98.9%, and 99.0%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection.