Abstract: Scientifically conducting village classification is an indispensable component for formulating and implementing township territorial and spatial planning, serving as a prerequisite for rural planning and the foundation for targeted execution of the rural revitalization strategy. Given China’s vast territory with significant regional disparities, place-specific classification methods are critical to accurately capturing local realities, addressing development demands, and advancing rural revitalization. Through comparative analysis of domestic and international approaches-focusing on factor selection and quantification techniques-it is observed that most studies prioritize constructing evaluation index systems. While these typically incorporate conventional factors（e.g., natural, humanistic, economic dimensions）, they frequently overlook distinctive local characteristics, resulting in incomplete indicator coverage, unbalanced weighting allocations, and neglect of villagers’ aspirations. To address these limitations, an ’n+X’ factor-selection model was developed: ’n’ denotes conventional factors（natural resources, population settlement, socio-economic conditions, village infrastructure）, while ’X’ signifies unconventional factors（historical culture, topographic traits, geological hazards）, collectively forming primary indicators. The Analytic Hierarchy Process（AHP） was applied to determine relative indicator weights, establishing a village development potential assessment model. In the case study of Maying Town, Longnan City, Gansu Province, an evaluation index system comprising 6 primary and 10 secondary indicators was constructed. Spatial overlay analysis via GIS yielded village development potential assessments, classifying 24 villages into four typologies: suburban-integrated, clusterenhanced, other, and relocation/merger. Given the locality’s high susceptibility to geological hazards, a ’one-vote veto’ mechanism against geological disaster risks was instituted to guide subsequent practical rural planning.