Scientometric Analysis of Artificial Intelligence in the Digital Economy

As the digital economy enters a period of deepening applications, artificial intelligence has become an important tool for reshaping the economic structure. Based on the Web of Science core collection database, this paper conducts a systematic scientiometric analysis of 494 papers in the field of artificial intelligence in the digital economy. By constructing keyword co-occurrence networks and clustering maps, this study reveals the evolution context and frontier hotspots of this field. The results show that: (1) The research hotspots focus on “AI-empowered green ESG”, “labor market reconstruction”, and “data element governance”, indicating that AI research is transforming from a simple technical efficiency orientation to a sustainable development and social governance orientation. (2) The time series map shows that the focus of research has shifted from the early “technical architecture construction” (such as cloud computing and Internet of Things) to the in-depth “scenario-based empowerment” (such as generative AI, metaverse, digital finance). (3) “Interactive AI”, “algorithm compliance,” and “cost optimization and efficiency enhancement” are the most promising research directions at present. This study objectively presents the knowledge structure of AI research in the digital economy, and also provides a scientific basis for understanding the economic externalities of AI technology.