Machine Learning Analysis of China's Digital Knowledge Transfer: Cultural and Material Engineering Perspectives

Songyu Jiang, Kanokporn Numtong and Han Wang
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Songyu Jiang: Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom, Thailand
Kanokporn Numtong: Faculty of Humanities, Kasetsart University, Bangkok, Thailand
Han Wang: Faculty of Teacher Traning, Xishuangbanna Vocational and Technical College, Jinghong City, China

International Journal of Customer Relationship Marketing and Management (IJCRMM), 2025, vol. 16, issue 1, 1-17

Abstract: This study employs machine learning to analyze the digital dissemination patterns of Chinese civilization with a focus on material engineering implications. The authors processed a corpus of 172 documents (1.6 million words) from WeChat using deep learning and LDA topic modeling, complemented by sentiment analysis of 7,370 comments. The analysis reveals key dissemination themes: (1) global impact of Chinese media and technology, (2) international education exchanges, (3) digital cultural identity evolution, (4) AI-mediated cultural transmission, and (5) engineering knowledge transfer. Sentiment analysis shows 62% positive engagement (particularly regarding technological integration and innovation), 28% neutral (technical descriptions and market analysis), and 10% negative (focusing on implementation challenges). The study provides a novel framework for understanding how digital platforms facilitate the global circulation of both cultural and engineering knowledge.

Date: 2025
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