Abstract: The mine ventilation system supplies fresh air to underground workings and promptly removes toxic and harmful gases such as methane, playing a vital role in ensuring the safety of personnel and maintaining stable underground airflow. To address the issue of insufficient airflow in return airways caused by excessive concentrations of toxic and harmful gases such as methane, this paper proposes a method for adjusting the required airflow in mine roadways based on a Multi-Strategy Cooperative Artificial Lemming Algorithm (MSCALA). Taking the required airflow of mine ventilation branches as the objective function, and considering constraints such as airflow balance and air pressure balance, a nonlinear optimization mathematical model is established. By calculating the airflow sensitivity matrix, the optimal set of resistance-adjustable branches and the range of airflow resistance adjustments are obtained. The required airflow branches are then optimized and adjusted based on the MSCALA algorithm. The results indicate that the MSCALA algorithm's mine network optimization method performs excellently in terms of optimization performance and stability, effectively addressing the issue of insufficient airflow when gas concentration exceeds limits in required air branches, and providing a new approach for intelligent mine ventilation management.