<p>The development and implementation of intelligent ventilation systems in the mining industry are advancing rapidly. For systems already in operation, ensuring their stability and reliability is critical to maintaining mine safety. To address this need, we propose a novel reliability assessment method for intelligent ventilation systems in underground mines, aiming to reduce potential failures and uncertainties, and thereby mitigate associated risks. We systematically identify the multi-dimensional factors influencing system reliability through literature review, the Delphi method, and systems engineering modeling. To enable quantitative analysis, fuzzy theory is introduced to enhance the coupling between the Decision-Making Trial and Evaluation Laboratory(DEMATEL) and Adversarial Interpretive Structural Modeling(AISM). Furthermore, a fault tree-Bayesian reliability assessment model is developed based on the mapping and coupling relationships among key factors. A case study was conducted on an operational intelligent ventilation system in a copper mine. The influence of each factor was ranked, and a multi-level hierarchical model was constructed to derive quantitative reliability evaluation results. The findings indicate that the system’s reliability corresponds to Grade III, and key contributing events have been identified. Overall, the proposed reliability assessment method enhances the efficiency of identifying critical factors and improves the accuracy of quantitative evaluations, providing more robust theoretical and practical support for risk analysis in similar engineering applications.</p>