Quantitative Analysis of Governmental Preferences Based on Text Mining: A Case Study of Industrial Development Plans in Chinese Airport Economic Demonstration Zones

Airport Economic Zones (AEZs) in China are largely guided by central and local government planning, and official planning documents provide important textual signals of industrial priorities. To identify these priorities, this study examines 17 national-level Airport Economic Demonstration Zones (AEDZs) and collects official documents on industrial development issued by central authorities and relevant local governments. Using dictionary-based named entity recognition, word-frequency analysis, clustering, and association rule mining, we quantitatively analyze stated governmental preferences in AEDZ industrial planning, focusing on emphasized industries and their combinations. The results show that local governments frequently emphasize industries prioritized by the central government, including aviation equipment manufacturing and maintenance, electronic information, aviation logistics, professional exhibitions, and e-commerce. They also tend to combine high-end intelligent manufacturing, electronic information technology services, and air cargo transportation in planning narratives. These patterns indicate policy alignment across AEDZs, while high similarity in stated industrial priorities may signal potential risks of redundant construction and resource misallocation if not matched with differentiated implementation. The findings are interpreted as textual policy signals rather than evidence of actual implementation or industrial performance.