This study takes the agricultural irrigation district of Changji, Xinjiang as the research object. By integrating time-series InSAR, geographically weighted regression (GWR), and wavelet analysis methods, and based on Sentinel-1A data from 2017 to 2020, the surface deformation field was obtained. Multi-source data were fused to quantify the spatial heterogeneity of driving factors, and periodic characteristics of the deformation time series were extracted. The results show that: 1)The deformation in the study area exhibits a spatial pattern of stable in the south, subsiding in the center,and uplifting in the north. The maximum subsidence rate in the central subsidence funnel reached -90.1 mm/a, while the uplift rate in the northern area reached 21.7 mm/a. Temporally, the deformation showed continuous accumulation superimposed with seasonal fluctuations, with the maximum cumulative subsidence reaching -407.3 mm in August 2020. 2)The GWR model revealed significant spatial heterogeneity in the driving mechanisms: stability in the south is controlled by abundant rainfall and coarse-grained aquifers; subsidence in the central area is closely related to groundwater over-extraction and fine-grained, easily compressible strata; uplift in the north is mainly driven by human-regulated water level recovery. 3)Wavelet analysis identified two main periodicities of 372 days and 348 days in the subsidence areas, whose spatial distribution is highly consistent with the intensity of agricultural irrigation. This indicates that agricultural extraction activities are the primary factor controlling the spatial differentiation of these periodic characteristics. This study provides a basis for differentiated prevention and control of land subsidence and adaptive water resource management in arid regions.