Mountainous open-pit mines are highly susceptible to landslides, yet quantitative risk assessment remains a challenge. This study aims to develop and validate a quantitative landslide risk assessment model by integrating multi-source data to enhance hazard identification in these complex environments. Taking the Dexing Copper Mine as a case study, we used Small Baseline Subset InSAR (SBAS-InSAR) to derive surface deformation rates. This deformation data was integrated with geological and topographical factors within a Geographic Information System (GIS), using an information value model combined with weighting from the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to generate a comprehensive landslide risk map. The results show that 3860 potential landslide points were identified, with deformation rates ranging from −338.74 to 80.61 mm/a. High and very high-risk zones were primarily concentrated around the Fujiawu and Zhujiawu dump sites, and the model’s performance was validated with a high degree of accuracy, achieving an Area Under the Curve (AUC) value of 0.871. This study demonstrates that the integration of multi-source data provides a robust and effective approach for quantitative landslide risk assessment in mountainous mining areas. The proposed framework can serve as a valuable tool for targeted disaster prevention and management.

Long-term exploitation of ion-adsorption rare earth deposits has played a vital role in ensuring the supply of strategic mineral resources. However, intensive mining activities have also resulted in severe ecological degradation, including vegetation loss, land degradation, and soil erosion. Although the Remote Sensing Ecological Index has been widely used for ecological environment assessment, it inadequately characterizes land degradation in ion-adsorption rare earth mining areas, while its fixed-weight framework is unable to effectively capture the influence of localized ecological limiting factors. To address these limitations, this study selected a typical ion-adsorption rare earth mining area in southern Jiangxi, China, as the study area. A Desertification Difference Index was incorporated into the conventional RSEI framework to establish a five-dimensional evaluation system consisting of greenness, wetness, dryness, heat, and desertification. Furthermore, a Dynamic Variable-Weight Remote Sensing Ecological Index (DV-RSEI) was developed by integrating variable-weight theory, enabling adaptive adjustment of indicator weights according to local ecological conditions. Using Landsat imagery from 2000, 2005, 2010, 2016, 2020, and 2023, the spatiotemporal evolution and spatial heterogeneity of ecological environmental quality were systematically investigated. The results indicate that: (1) ecological environmental quality exhibited a characteristic evolution process of mining disturbance–ecological degradation–comprehensive restoration–ecological recovery during 2000–2023, with an overall trend dominated by stability and improvement; (2) ecological environmental quality showed significant spatial clustering, with High–High clusters mainly distributed in areas with favorable ecological conditions, whereas Low–Low clusters were concentrated in regions strongly affected by mining activities; and (3) compared with the conventional RSEI, the DV-RSEI better characterized mining-related ecological degradation patterns and the spatial heterogeneity of ecological environmental quality. The proposed approach provides a scientific basis for dynamic ecological monitoring, evaluation of ecological restoration effectiveness, and the construction of green mines in ion-adsorption rare earth mining areas.

Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge.

Mine ecological restoration involves multiple tasks, including ecological problem identification, restoration measure selection, and restoration effect evaluation. Relevant knowledge is widely scattered across academic literature and policy and regulatory documents, making it difficult to directly support structured queries, knowledge association, and measure screening. To address the insufficient organization of knowledge in mine ecological restoration and the unstable quality of triples extracted by large language models, this study proposes a large language model-driven workflow for knowledge graph construction and restoration measure decision support in mine ecological restoration. First, a weak ontology schema containing eight entity types and eight relation types was constructed, and a manually annotated dataset was developed based on 30 papers on mine ecological restoration. Second, multiple large language models were compared on the triple extraction task, and Kimi, which achieved the best overall performance, was selected for large-scale knowledge extraction. On this basis, a weak-ontology-guided triple quality control method, named WOTQC, was proposed to check the structural consistency of candidate triples in terms of entity type, relation type, relation direction, and entity orientation. DeepSeek was then introduced to perform verification based on original textual evidence. Subsequently, entity normalization and relation aggregation were conducted on the verified triples to construct a knowledge graph for mine ecological restoration. Path mining, restoration measure decision support, and external case validation were further performed around the “ecological problem–restoration measure–restoration effect” path. The experimental results show that Kimi-based initial extraction achieved an F1 score of 0.7286. After WOTQC processing, the F1 score increased to 0.7561, and after DeepSeek-based evidence verification, it further improved to 0.7862. The final knowledge graph contains 18,783 entity nodes and 23,149 relations, and identifies 3048 “ecological problem–restoration measure–restoration effect” paths. The external case validation shows that the integrated ranking method achieved 1.00 under both strict Hit@3 and strict Hit@5, with a path explanation coverage@5 of 0.7500. The ranking results of the knowledge graph showed good correspondence with the main engineering measures in an actual mine ecological restoration design scheme. The results indicate that weak ontology constraints and verification based on original textual evidence can improve the reliability of triple extraction, while entity normalization and relation aggregation can enhance the structural consistency of the knowledge graph. The constructed knowledge graph can provide traceable and interpretable knowledge support for knowledge organization, candidate restoration measure screening, and restoration scheme formulation in mine ecological restoration, thereby supporting more informed and evidence-based restoration decision-making and contributing to the long-term ecological recovery and sustainable management of degraded mining areas.