Multi-Actor Conflict Identification and Governance Optimization in Urban Water-Ecological Systems Based on Knowledge Graph and Complex Networks

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks to regional ecological security. To address this challenge, this study develops a multi-actor governance analysis framework integrating deep learning, knowledge graphs, and complex network optimization. Stakeholder demands are extracted from multi-source data using a BERT-BiLSTM-CRF model, including policy documents, enterprise reports, and public discourse, and are then organized into a knowledge graph for water-ecological governance. A Relational Graph Attention Network (R-GAT) is subsequently used to transform the knowledge graph into a signed weighted network, enabling the measurement of conflict intensity and the identification of key conflict nodes across governance scenarios. Based on multi-objective optimization, a Pareto frontier is constructed to balance conflict tension, fairness, and governance efficiency, from which a compromise solution for responsibility weighting is identified. An empirical case study of a typical city in the Yellow River Basin shows that the proposed framework can identify core conflict nodes and provide quantitative support for conflict mitigation and coordination adjustment. The findings offer a quantitative reference for institutional innovation and evidence-based decision-making in urban water-ecological governance.