Ecological restoration in mining cities is essential for regional sustainable development, yet limited scientific understanding of its patterns and mechanisms has widened the gap between intended goals and actual outcomes. Taking Huangshi, a typical mining city, as a case study, this paper constructs an “elements-disturbances-status” (EDS) framework. By integrating interpretable machine learning, the study reveals the staged dynamics of city-wide ecological quality and its drivers, offering new perspectives for tailored restoration strategies. The results showed that: (1) ecological restoration followed a positive trajectory, advancing in three stages: negatively driven stage (2000–2010), rapid improvement stage (2010–2015), and stabilization and adjustment stage (2015–2023); (2) the excellent-grade areas followed a V-shaped trend (35.57% → 17.30% → 28.27% → 30.77%), while good-grade areas steadily expanded, forming a broad, high-quality ecological restoration landscape. Poor-grade areas shrank from 23.67% to 5.41%, indicating effective remediation of severely degraded zones. Although typical mining sites exhibited positive successional trends, ecological quality in core extraction zones warranted continued attention; (3) anthropogenic disturbances were the dominant drivers of spatial heterogeneity in ecological restoration. The ecological footprint of mining disturbances contracted over time, with the distance threshold for negative impact decreasing from 5 km to 3 km. However, annual precipitation exceeding 1419–1543 mm began to suppress ecological quality improvement. The persistent suppressive effect of high urbanization highlights the conflict between urban expansion and ecological protection, especially in areas with high precipitation. This study offers practical insights for enhancing ecological resilience and adaptive management in mining cities.
Li et al. (Tue,) studied this question.