Airport agglomeration coupling coordination is a key indicator of healthy regional aviation development. This study constructs an evaluation index system from three dimensions—airport production, infrastructure construction, and network support—and assesses the coupling coordination capability of China’s four major airport agglomerations using the entropy weight method and a coupling coordination model. Furthermore, an Airport Consistency Index is innovatively introduced as the reciprocal of the coefficient of variation, and an overall coordination degree is developed under the framework of “balanced average level + consistency correction.” By incorporating the inverse coefficient of variation, the proposed index explicitly assesses airport agglomeration dispersion in coordination performance, thereby mitigating the risk that a strong performance at leading airports masks structural imbalances within the system. This refinement enhances the diagnostic precision of the overall coordination assessment by integrating both average development level and internal convergence. Based on calculations for 2020–2024, the overall coordination ranking is Beijing–Tianjin–Hebei, Guangdong–Hong Kong–Macao Greater Bay Area, Yangtze River Delta, and Chengdu–Chongqing. The Beijing–Tianjin–Hebei agglomeration shows strong and stable coordination with limited sensitivity to external conditions, whereas the Yangtze River Delta is more environmentally sensitive due to its large number of airports. The Greater Bay Area demonstrates solid coordination with substantial synergy potential, while Chengdu–Chongqing exhibits relatively weak coordination and considerable room for improvement. The proposed model effectively evaluates the overall coordination degree of airport agglomerations and supports targeted development recommendations.

To address the limitations of existing approaches that rely primarily on individual operational indicators and provide limited insight into the dynamic evolution of airport operational states, this study develops a two-stage analytical framework integrating unsupervised state identification and supervised temporal classification. Using monthly operational data from seven major Japanese airports between January 2010 and March 2024, the proposed framework first applies K-means clustering to identify latent operational states based on multidimensional indicators reflecting growth dynamics, operational efficiency, and relative operational level. Three distinct states are identified: a low-activity state, a recovery state, and a rapid-rebound state. Here, the rapid-rebound state denotes a temporary operational regime characterized by exceptionally strong year-on-year growth from a comparatively depressed base, rather than the highest absolute utilization rates or 2019-relative operational level. The results reveal clear stage-specific characteristics and transition patterns, with the recovery state serving as the most persistent and relatively stable operational regime, while rapid-rebound episodes are less frequent, less stable, and often transition back to the recovery state. The supervised component subsequently evaluates whether recovery and rapid-rebound labels retrospectively identified through full-sample clustering can be distinguished using lagged operational characteristics. Six classification algorithms, including logistic regression, radial basis function support vector machine (RBF-SVM), random forest, gradient boosting, XGBoost, and LightGBM, are compared using a chronologically ordered 60/20/20 partition applied only at the supervised-classifier stage, with SMOTE applied exclusively to the training subset. Because the standardization parameters, K-means solution, and target state labels were derived from the complete 2010–2024 sample, and because several 2019-relative variables were constructed using an ex post benchmark, the reported results represent classifier-stage temporal evaluation of retrospectively identified full-sample labels rather than end-to-end out-of-sample validation or prospective real-time prediction. Under the prespecified common SMOTE-based six-model comparison, LightGBM yields the highest point estimates for macro-F1 and rapid-rebound-state recall, reaching 0.6864 and 0.5588, respectively. Given that the training subset contains only seven rapid-rebound observations, these estimates should be interpreted cautiously. An exploratory sensitivity analysis using alternative imbalance-handling strategies produces materially different point estimates, indicating that minority-state classification performance is sensitive to the selected imbalance treatment. The SHAP-based feature attribution results indicate that short-term lagged growth indicators and twelve-month 2019-relative operational-level variables contribute strongly to the fitted distinction between the recovery and rapid-rebound state labels. Although the supervised analysis is restricted to distinguishing the recovery and rapid-rebound state labels, the proposed framework provides a multidimensional approach for airport state identification, temporal evolution analysis, retrospective operational monitoring, and post-shock recovery assessment. A genuinely prospective application would require reconstructing all input variables using information available at each forecast origin and re-estimating the models accordingly.