Abstract
The deepening of the digital economy is reshaping tourism markets in fundamental ways, particularly in transaction architectures, algorithm-driven price formation, and platform-centered governance. While platformization and algorithmization can improve allocative efficiency, they also intensify classic market failures—such as information asymmetries, discriminatory pricing, and credibility deficits—by making them more scalable, dynamic, and difficult to observe. Conventional macroeconomic regulation, which relies primarily on manual inspections and ex post enforcement, is increasingly mismatched with high-frequency, data-intensive market environments. This paper develops a governance-oriented analysis of how artificial intelligence (AI), with a focus on machine learning and big data analytics, can support tourism macroeconomic regulation. It explains the mechanisms through which AI contributes to identifying market-failure patterns, enabling risk early warning and regulatory prioritization, strengthening credit governance and market discipline, and improving policy evaluation through data-driven feedback. Rather than treating AI as a substitute for public authority, the paper conceptualizes AI as capacity-enhancing infrastructure that expands regulatory cognition by improving information processing, detection precision, and response timeliness. Integrating market failure theory with perspectives on algorithmic regulation and digital governance, this paper proposes a “technical support–institutional embedding–policy feedback” framework. The analysis suggests that, under appropriate legal safeguards and ethical constraints, AI can mitigate structural failures in tourism markets and enhance the resilience and effectiveness of macroeconomic governance. The study provides theoretical grounding for building data-driven, precision-oriented tourism regulation and offers actionable implications for upgrading regulatory toolkits in digital markets.