Promoting the coordinated development of rural multifunctionality in the Yellow River Basin is a key pathway toward achieving regional rural revitalization and building a livable, business-friendly, and aesthetically pleasing countryside. Using 107 counties and districts in Shaanxi Province as the basic research units and selecting the years 2010, 2015, and 2020 as temporal nodes, this study constructs a rural multi-functionality evaluation system. By integrating subjective and objective weighting methods with the GeoDetector model, it explores the spatiotemporal evolution of rural functions and the driving mechanisms behind dominant functional types. The research results reveal 3 findings. ① High-value multifunctional areas exhibit significant spatial heterogeneity. Agricultural production functions have evolved into a "multi-point scattered + clustered" pattern; non-agricultural production functions show a "multi-point mosaic" pattern; livelihood security functions remain stable in non-central areas of Guanzhong and northern Shaanxi; and ecological conservation functions display a "high in the south, low in the north" distribution, with a trend of "simultaneous decline in both high and low values." ② Based on current dominant functions and their interactions, dominant functional types are categorized into four major classes and twelve subtypes, reflecting a transition from single-function dominance to multifunctional integration. ③ Univariate factor detection indicates that dual-function dominant areas are influenced by fewer driving factors, yet these factors exert stronger effects. Transportation accessibility, production conditions, and the degree of marketization are key constraints limiting the enhancement of weakly integrated multifunctional areas. Interaction analysis reveals significant synergistic effects among influencing factors, characterized by nonlinear enhancement and bivariate enhancement patterns. The findings of this study can provide a scientific basis and decision-making support for the future functional orientation and strategic planning of rural development in Shaanxi Province and the broader Yellow River Basin region.

China officially entered an era of negative population growth in 2022. This paper utilizes data from the fifth, sixth, and seventh national population censuses and applies methods such as spatial autocorrelation analysis and OLS linear regression to analyze the spatio-temporal characteristics and influencing factors of population shrinkage in the Shaanxi section of the Yellow River Basin from 2000 to 2020 at the city, county, and village scales. On this basis, a multi-scale framework for the mechanisms of population shrinkage in the Shaanxi section of the Yellow River Basin is constructed. The results indicate the following: ① Population shrinkage exhibits spatial heterogeneity at different scales, and spatial quantification at higher scales does not represent the spatial dynamics at lower scales. ② In northern Shaanxi, a "T"-shaped shrinkage zone has formed, consisting of an eastern shrinkage belt and a shrinkage zone at the junction of Yulin and Yan’an city. The shrinkage axis in the Guanzhong region primarily radiates outward from the central urban area of Xi’an city and Chang’an District in a concentric diffusion pattern. ③ At the county scale, industrial structure is the fundamental driving force in curbing population shrinkage, while aging and population decline are mutually reinforcing. Provincial-level aggregation and public service levels are correlated with county-level shrinkage. ④At the village scale, industrial development is the key factor in mitigating shrinkage. Population structure is significantly related to village-level shrinkage, while land intensification, primary education, and geographic clustering are highly correlated with village shrinkage. The findings of this study provide scientific support for the spatial development and protection of the Shaanxi section of the Yellow River Basin and hold practical significance for promoting high-quality development in the region.