This paper employs bibliometric analysis to deeply explore the evolution of the knowledge structure and its visual representation in the integration of obesity management and artificial intelligence (AI), identify key research topics, development trajectories, and frontier directions, and provide evidence-based support for the clinical application of endocrinology and interdisciplinary integration.

Using the Web of Science core database as the data source, the search strategy of "Obesity" OR "Weight Loss" AND "Artificial Intelligence" OR "Machine Learning" was adopted, covering Article and Review types from 2011 to 2025. CiteSpace was utilized for analysis, with annual time slices set and the Top 50 per slice screening criterion applied to construct co-occurrence networks of terms, cluster lineage diagrams, temporal evolution diagrams, and author-institution collaboration maps.

The number of documents showed an exponential growth trend, going through three stages: initial accumulation, rapid development in the middle period, and explosive growth in the recent period. The co-occurrence network of terms included 651 nodes and 2,935 connections, with a network density of 0.0139. High-frequency terms included "obesity" (1 241 times) and "machine learning" (403 times). The clustering results revealed 10 research modules (Q = 0.4151, S = 0.6977), with the main research dimensions focusing on the application of AI technology (deep learning), mechanism explanation (metabolic syndrome), and treatment intervention (weight loss surgery). The temporal evolution showed that the research focus shifted from the early epidemiological foundation to the budding of clinical application in the middle period, and then to the recent precise integration and development, such as the combination of gut microbiota and AI prediction models. In the institutional collaboration network, Harvard University held a core position, while Chinese research institutions demonstrated a catching-up development trend.

The knowledge evolution in this research field reflects the model innovation of AI technology from risk assessment to personalized treatment. Current hotspots have shifted to explainable AI and multimodal data fusion. Future research should strengthen international collaboration, promote clinical randomized controlled trials for validation, pay attention to ethical norms and algorithm bias issues, and enhance the global effectiveness of obesity prevention and treatment and the development of precision medicine.