文献类型：期刊文献

英文题名：Protection of Guizhou Miao batik culture based on knowledge graph and deep learning

作者：Quan, Huafeng[1];Li, Yiting[1,2];Liu, Dashuai[3];Zhou, Yue[4]

机构：[1]Guizhou Univ Finance & Econ, Coll Big Data Stat, Guiyang 550025, Peoples R China;[2]Guizhou Univ, Key Lab Adv Mfg Technol, Minist Educ, Guiyang 550025, Peoples R China;[3]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[4]Guizhou Elect Technol Coll, Guiyang 550025, Peoples R China

年份：2024

卷号：12

期号：1

外文期刊名：HERITAGE SCIENCE

收录：;EI(收录号：20240180742);WOS:【SCI-EXPANDED(收录号:WOS:001249135300002)，A&HCI(收录号:WOS:001249135300002)】；

基金：The authors are appreciated for anonymous reviewers' insightful comments.

语种：英文

外文关键词：Guizhou Miao batik; Cultural heritage; Knowledge graph; ResNet34; Word2vec

摘要：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 categories 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 94.46%, 94.47%, 93.62%, and 93.8%, 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.