Thematic Evolution of Artificial Intelligence Research in Chinese-Speaking Academia (2021–2025): A Bibliometric and Text-Mining Analysis Using VOSviewer and KH Coder

Digital Technologies Research and Applications

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Thematic Evolution of Artificial Intelligence Research in Chinese-Speaking Academia (2021–2025): A Bibliometric and Text-Mining Analysis Using VOSviewer and KH Coder

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https://doi.org/10.54963/dtra.v5i1.1960
Sheng-Ming Wang, & Chuding Chen. (2026). Thematic Evolution of Artificial Intelligence Research in Chinese-Speaking Academia (2021–2025): A Bibliometric and Text-Mining Analysis Using VOSviewer and KH Coder. Digital Technologies Research and Applications, 5(1), 1–20. https://doi.org/10.54963/dtra.v5i1.1960
Volume 5 Issue 1: March 2026 (in progress)
Copyright (c) 2026 Sheng-Ming Wang, Chuding Chen
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Authors

  • Sheng-Ming Wang

    Department of Design, National Taipei University of Technology, Taipei 106, Taiwan
  • Chuding Chen

    Department of Design, National Taipei University of Technology, Taipei 106, Taiwan

Received: 29 November 2025; Revised: 29 December 2025; Accepted: 7 January 2026; Published: 20 January 2026

This study examines the visible thematic patterns of artificial intelligence (AI)–related research in Mainland China, Taiwan, Hong Kong, and Macao from 2021 to 2025. The analysis is based on Scopus-indexed journal articles and uses keyword frequency counts, VOSviewer density visualizations, and KH Coder co-occurrence networks. These methods are applied to describe how AI-related keywords appear across the four regions and how their distributions change during the five-year period. Across all datasets, terms such as machine learning, deep learning, and neural network appear frequently and occupy central positions in the visual outputs. The VOSviewer heatmaps show that regions with larger publication volumes display wider areas of keyword density, while regions with smaller datasets present more compact clusters. Beginning in 2024 and 2025, generative AI–related terms, including large language model and ChatGPT, become visible across all regions. The KH Coder networks illustrate that the four regions contain multiple clusters of co-occurring keywords, with differences in cluster size and distribution reflecting the underlying dataset scale and the topics present in each regional corpus. Overall, the results offer a descriptive account of how AI-related terms appear in the collected datasets and how their visible distributions vary among the four regions during the study period. The findings are intended to summarize observable patterns without inferring causal explanations or evaluating the significance of regional differences.

Keywords:

Artificial Intelligence Bibliometric Analysis VOSviewer KH Coder

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