Fire is the core safety threat to the survival and development of timber-framed buildings, and passive fire prevention intervention is the core foundation of fire protection systems for timber-framed buildings. Existing reviews suffer from limitations such as incomplete scenario coverage, insufficient breakdown of intervention mechanisms, and a lack of methodological standardization. This study strictly followed the PRISMA 2020 systematic review guidelines, searching the relevant literature from January 2016 to April 2026 on the Web of Science, Scopus, and Science Direct databases. After standardized screening, 89 valid articles were finally included and a systematic study was conducted through bibliometric analysis, keyword visualization, and multi-dimensional classification coding. The results show that the number of publications in this field has been continuously increasing from 2016 to 2025, with China accounting for 31.46% of the total, ranking first globally. The study constructed a core intervention mechanism system for passive fire prevention in timber-framed buildings, covering four categories: intrinsic flame-retardant modification, isolation protection, structural optimization, and spatial control. The working principles, application effects, advantages and disadvantages, and engineering application scenarios of each mechanism were clarified. This study systematically sorts out the core intervention mechanisms of passive fire prevention in timber-framed buildings, clarifies the research status and development trends in this field, and can provide evidence-based support for the design optimization, technology development, and engineering practice of passive fire protection for timber buildings.

Traditional fire detection technologies for buildings are no longer adequate for the fire prevention and control needs of complex structures, while emerging artificial intelligence technologies have become the core path to break through the bottlenecks in this industry. Existing reviews suffer from non-standardized paradigms, one-sided scopes, insufficient methodological evaluation, incomplete time coverage, and a lack of engineering orientation, failing to meet the evidence-based research needs of the field. This study strictly followed the PRISMA 2020 systematic review guidelines, selected relevant SCI papers from the Web of Science Core Collections from 1 January 2016 to 30 March 2026, in JCR Q1 and Q2, and finally included 221 valid papers; it systematically carried out bibliometric analysis and technical system sorting. The results showed that (1) the number of publications in this field showed a significant exponential upward trend, with China accounting for 52% of the research output, ranking first in the world, and (2) convolutional neural networks and YOLO series algorithms are the mainstream application technologies in the field. The study clarified the performance differences, advantages, and disadvantages, and applicable scenarios of various artificial intelligence algorithms. This study identified the existing technical and methodological limitations in the field and explored the core research directions for the future, and provided evidence-based support for the academic research and engineering implementation of artificial intelligence in the field of building fire detection and prevention.