以新疆干旱区14个地州为例，基于压力-状态-响应（PSR）模型构建评价指标体系，运用熵权法计算指标权重，采用综合评价法和核密度分析14个地州压力-状态-响应韧性的时空分布特征，借助地理探测器对新疆水资源经济生态韧性的主要影响因子及因子交互作用进行探测。结果表明：（1） 2010—2020年新疆各地州压力韧性评价指数多数呈现下降的趋势，其中克孜勒苏柯尔克孜自治州的压力韧性水平由高度韧性降为中等韧性。状态韧性、响应韧性、综合韧性均呈现不断增强的趋势，韧性水平也有所提升。（2） 2010—2020年新疆各地州压力-状态-响应韧性核密度空间上呈现高低交错分布的格局，且西南部高于东北部。从3个维度来看，压力韧性核密度高值区由西南部向中部蔓延，分布较集聚；状态韧性和响应韧性核密度高值区由北部向南部蔓延，分布较分散。2020年，区域内综合韧性、状态韧性、响应韧性核密度差异呈不断缩小的趋势，压力韧性核密度空间差异较显著。（3） 产业结构、人均GDP、生态自净能力影响因子对水资源经济生态韧性的影响力有所增强，人为灾害等因素有所减弱。产业结构、生态自净能力、社会消费品零售额等影响因子的交互作用强于单因素对系统韧性的影响力，各影响因子两两之间的交互作用中非线性增强关系数量大于双因子增强数量。

This study employs the entropy weight method to determine index weights, utilizing the comprehensive evaluation method and kernel density to assess the spatial and temporal distribution characteristics of pressure-state-response resilience in 14 prefectures within the Xinjiang arid zone of China. In addition, geographic detectors are used to analyze the main influencing factors and factor interactions affecting the economic and ecological resilience of water. The findings reveal the following trends: (1) Stress resilience evaluation indices for Xinjiang prefectures generally decline from 2010 to 2020. Specifically, Kizilsu Kirgiz Autonomous Prefecture experiences a shift from high resilience to moderate resilience. Conversely, state resilience, response resilience, and comprehensive resilience exhibit an increasing trend, reflecting an overall improvement in resilience levels. (2) The spatial distribution of pressure-state-response resilience kernel density across all Xinjiang prefectures from 2010 to 2020 displays a staggered pattern, with higher values concentrated in the southwest compared to the northeast. Analyzing the three dimensions reveals a concentrated distribution of high-pressure resilience kernel density moving from the southwest to the central region. Meanwhile, high state resilience and response resilience kernel densities progress from the northeast to the southwest, displaying a more dispersed distribution. Toward the end of the study period, comprehensive resilience, state resilience, and response resilience kernel densities exhibit a decreasing trend, while spatial differences in pressure resilience kernel densities become more pronounced. (3) The influence of industrial structure, GDP per capita, and ecological self-purification capacity on water resources’ economic and ecological resilience has intensified, while the impact of man-made disasters and other factors has weakened. Interactions among industrial structure, ecological self-cleaning capacity, and retail sales of social consumer goods are more influential than individual factors in shaping system resilience. Notably, the number of nonlinear enhancement relationships in factor pairs surpasses the number of two-factor enhancements.