Research on forest fire risk assessment and prevention and control Countermeasures: A case study of Qinyuan County, Shanxi Province, China

• Comprehensive Risk Assessment System: This study establishes a detailed forest fire risk evaluation system for Qinyuan County, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Integrated Evaluation Method (FIEM) to assess risk levels across 12 regions, providing a scientific basis for targeted fire prevention strategies. • Visualization and Zoning Analysis: Utilizing ArcGIS, the study visualizes forest fire risk zoning, revealing that 50.97% of the county faces medium risk and 49.03% medium–high risk, offering actionable insights for resource allocation and emergency planning. • Practical Prevention Strategies: The research identifies key challenges in fire prevention, such as inadequate infrastructure and monitoring systems, and proposes actionable countermeasures, including enhanced early warning systems and improved fire road networks, to mitigate risks effectively. Forest fires are characterized by their widespread occurrence, high frequency, significant uncertainty, immense destructiveness, and challenging management. Qinyuan County in Shanxi Province has dense forests with highly flammable coniferous species. Its climate and topography increase wildfire risks. Although ecologically important, the area lacks local fire risk assessments and effective mitigation strategies. This study establishes a comprehensive forest fire risk evaluation system for Qinyuan County by integrating the Analytic Hierarchy Process (AHP), Delphi method, and multi-level Fuzzy Comprehensive Evaluation Method (FCE) to assess 12 townships. Risk zoning maps were generated via ArcGIS, revealing 50.97 % of the county as medium-risk (130,130.69 ha) and 49.03 % as medium–high-risk (125,168.69 ha). Key innovations include a tailored indicator system addressing disaster-causing factors, vulnerability, exposure, and mitigation capacity, coupled with spatially explicit risk visualization. The findings highlight critical gaps in infrastructure, early warning systems, and fire management, enabling data-driven countermeasures. This research provides a replicable framework for fire-prone regions with similar environmental conditions, enhancing risk management and supporting sustainable forest conservation.