To objectively evaluate the differences in AI industrial policies across regions and enhance the uniqueness, rationality,

comprehensiveness, and scientific rigor of policy measures, this study conducts a comparative analysis based on textual data from national, Xi'an, Jinan, and Chengdu AI industrial policies (2017–2024). Employing text mining techniques for word frequency statistics, we construct a Policy Modeling Consistency (PMC) index model comprising 10 primary and 47 secondary variables, supplemented by a grey relational analysis model to quantitatively assess policy heterogeneity among the three cities. Key findings include: (1) Divergent approaches in incentive policies—Xi'an emphasizes financial support and technical guarantees, Jinan prioritizes policy frameworks, while Chengdu focuses on fiscal incentives. (2) All three cities align their policy priorities with the national "New Generation Artificial

Intelligence Development Strategy," while incorporating local characteristics. (3) Innovation and technology emerge as central themes across all regional policies. By integrating the PMC index model and grey relational analysis, this study systematically compares inter-regional policy heterogeneity and proposes actionable recommendations, including refining intellectual property laws and regulatory frameworks, optimizing talent cultivation systems, and fostering robust innovation ecosystems.