Against the backdrop of rapid advances in artificial intelligence (AI) and the accelerating reshaping of the global technological competition landscape, interdisciplinary integration has increasingly become a critical driver of technological breakthroughs and knowledge innovation. Particularly amid intensifying Sino-American technological rivalry, comparing the differences in AI interdisciplinary knowledge integration paths across countries is of significant practical relevance for optimizing innovation strategies and enhancing competitive resilience. However, existing studies have predominantly focused on AI theory development, technological innovation, and overall trends, with relatively limited systematic exploration of AI’s interdisciplinary knowledge system and its evolutionary mechanisms. This gap partially constrains understanding of the intrinsic patterns of AI development and, consequently, limits the ability to seize strategic initiative.

This study draws on the Web of Science (WoS) database, selecting 89,448 valid publications up to December 31, 2024, and constructs an integrated research framework encompassing “topic identification - structural analysis - mechanism comparison”. First, employing the BERTopic modeling method, the study identifies representative interdisciplinary topics in AI and characterizes their features in terms of knowledge sources, integration depth, and structural types by introducing disciplinary diversity and cohesion indicators. Second, by analyzing changes in topic intensity and content evolution, it reveals the evolutionary paths and coupling patterns of interdisciplinary knowledge across different stages. Finally, using the United States and China as comparative cases, the study examines differences in topic distribution, structural patterns, and driving mechanisms, exploring how institutional logic, organizational forms, and resource allocation shape interdisciplinary integration.

The findings indicate that the AI field has formed a complex interdisciplinary knowledge system composed of 23 heterogeneous topics, with significant variation across topics in disciplinary sources, integration depth, and structural complexity, displaying a combination of parallel paradigms, heterogeneous integration, and structural coupling. The evolution of interdisciplinary knowledge typically follows a cyclical process, progressing from the introduction of heterogeneous elements to the integration of methodological paradigms and the differentiation of new subfields, with coupling realized through multi-layered nested coordination between core and interface disciplines. International comparison shows that the U.S. emphasizes paradigm-driven, system-level diffusion, relying on open collaboration and multi-actor participation to promote parallel exploration across multiple directions, whereas China exhibits an engineering- and application-driven pattern, emphasizing concentrated resource mobilization and rapid integration, resulting in goal-focused and outcome-oriented pathways. These mechanistic differences not only influence the pace and direction of knowledge evolution but also shape each country’s relative advantages in frontier leadership and applied breakthroughs.

The study proposes high-level strategic directions for enhancing China’s interdisciplinary integration capacity and systemic innovation resilience in AI, including the development of heterogeneous coupling platforms, optimization of project and collaboration mechanisms, and the cultivation of interface discipline ecosystems. The research systematically extends the characterization of interdisciplinary knowledge structures, the identification of evolutionary paths, and comparative analysis of U.S. and Chinese mechanisms, offering a generalizable analytical framework and methodological approach. It deepens understanding of the patterns and mechanisms of interdisciplinary integration in AI and provides a novel theoretical perspective for studying the complexity of technological convergence. By elucidating cross-national differences in interdisciplinary innovation paths and driving logics, the study provides scholarly support and practical guidance for formulating forward-looking AI development strategies and strenthening competitiveness global technological competition.