Abstract: This paper evaluates the high-quality development of national central cities by proposing a novel grey clustering method for the comprehensive assessment of their developmental levels. This approach facilitates dynamic analysis of urban development status and contributes to the theoretical framework of grey evaluation methods, thereby providing a robust foundation for urban development decision-making. The proposed method addresses the limitations inherent in traditional grey possibility function clustering models, particularly their inadequacy for panel data analysis and their lack of dynamic measurement capabilities in evaluating indicator systems. The novel model incorporates time weighting by optimizing the weights of time point index attributes to derive time domain attribute weights. For panel data, observed values are integrated into a comprehensive evaluation framework, facilitating dimension reduction and the transformation of panel data into cross-sectional data. Subsequently, grey clustering analysis is applied to the cross-sectional data. The validity and feasibility of the proposed model in handling panel data are verified through an evaluation and analysis of the high-quality development of national central cities. 

Key words: Dynamic contribution degree, Grey possibility degree, Panel data, Attribute weight, Clustering