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Analyzing the evolutionary trajectory of technological themes based on the BERTopic model: A case study in the field of artificial intelligence

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  • Xinle Zheng
  • Qing Wu
  • Xingyu Luo
  • Kun Lv

Abstract

As the wave of technological innovation propels national development into the future, technological advancement has emerged as a crucial pillar for enhancing international competitiveness. Unraveling the evolutionary trajectory of technologies and their associated themes provides a solid theoretical foundation for strategic decision-making in fostering future industrial technological upgrades, thereby aiding in seizing the initiative in technological innovation. This study, adopts a multi-source data perspective, employing the life cycle theory to delineate temporal windows. We use the BERTopic model to extract technological themes and construct a co-occurrence network of theme keywords. Three network centrality indices are computed to filter key theme terms, and the Word2Vec model is leveraged to calculate cosine similarities. Ultimately, we map out the evolutionary pathway of technological themes using Sankey diagrams. Taking the field of artificial intelligence as an example, the study found that the proposed method could effectively identify 48 technical theme keywords and analyze the technological evolution paths of these keywords in areas such as scenario applications, network services, human-computer interaction, intelligent detection, and natural language processing. Furthermore, all evaluation metrics of the model outperformed those of comparable topic models. The rationality of the empirical results was validated through examination against national policies and market application scenarios.

Suggested Citation

  • Xinle Zheng & Qing Wu & Xingyu Luo & Kun Lv, 2025. "Analyzing the evolutionary trajectory of technological themes based on the BERTopic model: A case study in the field of artificial intelligence," PLOS ONE, Public Library of Science, vol. 20(6), pages 1-23, June.
    • Handle: RePEc:plo:pone00:0324933
    DOI: 10.1371/journal.pone.0324933
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    References listed on IDEAS

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    1. Augustinus, Clarissa, 2020. "Catalysing global and local social change in the land sector through technical innovation by the United Nations and the Global Land Tool Network," Land Use Policy, Elsevier, vol. 99(C).
    2. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    3. Xiwen Liu & Xuezhao Wang & Lucheng Lyu & Yanpeng Wang, 2022. "Identifying disruptive technologies by integrating multi-source data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5325-5351, September.
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