Fine-Grained Village Functional Differentiation in Rural Territorial Systems: A Few-Shot Hierarchical Graph Learning Approach

Identifying village functional differentiation within rural territorial systems is essential for differentiated rural revitalization and place-based governance. However, existing approaches still lack effective analytical pathways for translating complex rural territorial relations and sparse planning labels into fine-grained measures of rural functional intensity. To address these gaps, this study develops a Few-Shot Hierarchical Graph Representation Learning (FH-GRL) framework. By integrating a Hierarchical Graph Infomax (HGI) model to capture cross-scale village–township–city relational dependencies and an Evidential Deep Learning (EDL) mechanism to map high-dimensional representations into class-specific evidence and Global Percentile Ranks (GPR), the framework supports fine-grained classification and continuous grading of rural functions. Empirical analysis in Pingdingshan City yields three main findings. First, within the present case study, FH-GRL shows more stable performance than traditional flat clustering and local graph models in identifying complex rural functions under limited labeled samples. Second, hierarchical context serves as a spatial calibration mechanism, reducing locally generated noise and improving the identification of village functional differentiation under spatial heterogeneity. Third, rural functional differentiation reflects the combined effects of place-based conditions and potential flow-related interaction conditions. In particular, Center villages show differentiated trajectories between endogenous production or service centers in agricultural plains and exogenous service centers along urban development axes. Overall, this study provides a planning-oriented quantitative framework for diagnosing rural functional differentiation under label scarcity and spatial heterogeneity. The GPR-based outputs can support the identification of high-intensity functional carriers, transitional villages, and general reserve areas, thereby providing diagnostic evidence for differentiated governance and tiered resource allocation. Rather than replacing formal planning judgment, the framework offers geospatially informed support for classified rural governance and more evidence-informed territorial planning.