Quantitative Model Construction for Sustainable Security Patterns in Social–Ecological Links Using Remote Sensing and Machine Learning

With the global issues of extreme climate and urbanization, ecological security patterns (ESPs) in Qinling Mountains are facing prominent challenges. As a crucial barrier China, understanding characteristics ESPs is vital for achieving sustainable development. This study focuses on Yangxian employs methods such as machine learning (ML), remote sensing (RS), geographic information systems (GISs), analytic hierarchy process principal component analysis (AHP–PCA), minimum cumulative resistance (MCR) model to construct an network based multi-factor sensitivity (ES) conduct quantitative spatial analysis. The results demonstrate that AHP–PCA method ML overcomes limitations single-weighting method. were established, consisting 21 main secondary sources with area 592.81 km2 (18.55%), 41 corridors length 738.85 km, 33 nodes. A coupling relationship among three dimensions was observed: comprehensive sensitivity, ESPs, administrative districts (ADs). Huangjinxia Town (1.43 C5) Huayang (7.28 C4) likely have significant areas vulnerability, while Machang Maoping important ESPs. ADs focus protection management. second corridor indicated high-quality construction, necessitating implementation strict policies area. innovation lies utilization methods, RS technologies, pattern planning propose new perspective space. provides foundation urban rural will help facilitate development region.