Analysis of Government AI Research Topic Mining 
based on BERTopic Modeling

Chengcheng Li; Xinrui Wang

doi:10.70711/aitr.v2i9.6859

Analysis of Government AI Research Topic Mining based on BERTopic Modeling

Chengcheng Li, Xinrui Wang

Abstract

This study aims to explore the research themes and their development trends in the field of government artificial intelligence in the
past decade (2015-2024), which can provide a reference for relevant research decisions. By obtaining 5, 715 government AI-related literature
titles from the CNKI database, we use the BERTopic model to mine the research themes of the literature, and classify the stages of government AI development based on the logical growth law of the literature information, and then analyze the evolutionary characteristics of the
research themes. The research results show that 20 directions, such as smart city and community governance, smart elderly care and service,
healthcare and AI, digital agriculture and rural revitalization, have become the main research areas. Multiple factors such as the breakthrough
of deep learning technology, the requirements of government governance modernization, and the advancement of smart city construction have
combined to promote the continued expansion and deepening of government AI research. In recent years, the rise of generative AI has injected
new vitality into the field, further enriching the research content and expanding the application scenarios.

Keywords

Government artificial intelligence; BERTopic; Government governance

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References

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DOI: http://dx.doi.org/10.70711/aitr.v2i9.6859

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