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Machine Learning Analysis of China's Digital Knowledge Transfer

Year: 2025

Type: article

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.

Source: International Journal of Customer Relationship Marketing and Management

DOI: 10.4018/ijcrmm.390273

Authors: Songyu Jiang, Kanokporn Numtong, Han Wang

Institutions: Rajamangala University of Technology Rattanakosin, Kasetsart University, Xishuangbanna Vocational and Technical College

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4415127115

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