ArticlePloS one2025
Analyzing the evolutionary trajectory of technological themes based on the BERTopic model: A case study in the field of artificial intelligence.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Ketogenic Diet and Neurological Health: A Narrative Review with Natural Language Processing-Assisted Thematic Mapping Using BERTopic.Neuropsychiatric disease and treatment · 2026Review
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Authors and funding
4 authors.
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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.
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