Abstract: Existing studies on intelligent mine ventilation still show deficiencies in accurate perception of key parameters, coupled processing of multi-source information, and coordinated regulation, and have not yet formed an integrated technical system, which makes it difficult to meet the demand for refined and intelligent management of mine ventilation systems. To address these problems, an integrated system architecture of "perception-transmission-analysis-decision-application" was adopted, and an intelligent mine ventilation system was de-signed. The system adopted a wind velocity monitoring method based on the ultrasonic time difference principle and a wind pressure monitoring method based on piezoresistive Micro-Electro-Mechanical Systems (MEMS), achieving high-precision online perception of ventilation parameters. Through structural optimization of air doors and air windows, variable frequency drive of power equipment, and multi-parameter perception and intelligent control technologies, remote coordinated control of ventilation facilities and collaborative regulation of power equipment were realized. Combined with multi-source information fusion, ventilation network calculation, Radial Basis Function (RBF) neural-network-based identification, and fuzzy inference methods, abnormal diagnosis of the mine ventilation network was conducted, enabling identification of ventilation anomalies, determination of their locations, and assessment of the affected range of disasters. Backpropagation (BP) neural network and Particle Swarm Optimization (PSO) algorithms were used, together with a dynamic weight optimization mechanism, dynamic prediction of required air volume and global optimal regulation of the ventilation network were achieved. Field application results showed that the maximum relative error of air volume calculation was 8.38%, and the average relative error of wind resistance calculation was 2.11%, indicating that the system accurately reflected the operating state of the mine ventilation network, and automatically performed emergency ventilation control and guided personnel evacuation based on disaster information, effectively improving the intelligent management level and intrinsic safety capability of the mine ventilation system.