Abstract: This study aimed to address challenges associated with diverse dam failure triggers, complex transmission pathways, and difficulties in quantifying associated losses. By leveraging Scrapy-based web crawlers and literature analysis to collect multisource data, we developed regular expression sets to extract transmission pathways, which were categorized into four major types: meteorological, geological, engineering/management, and biological. Quantum encoding, incorporating failure potential energy and frequency factors, yielded four topological configurations applicable to risk assessment. A credibility evaluation mechanism was introduced to validate the robustness and reliability of the quantum topology analysis. The results demonstrate that the knowledge graph extracted 70 typical pathways, whereas quantum topology analysis revealed that engineering/management factors exhibited the highest topological vulnerability index, followed by meteorological and geological factors, with biological factors showing the lowest values. Path credibility aligns with node connectivity patterns, and topological vulnerability levels correspond to actual disaster mechanisms across different risk categories. The proposed “graph-topology” technical paradigm enables the structured integration and quantification of dam failure risk transmission pathways, thereby providing a novel analytical framework for risk pathway evaluation.