Abstract: At present, most researches on mine air volume regulation are single-branch regulation, which cannot meet the requirement of large air volume of a specific wind branch. Therefore, it is necessary to distribute air on demand through multiple branch joint regulation. Based on the nodal air volume balance law, the loop air pressure balance law and the minimum air volume constraint conditions of each branch, the mine air volume regulation model is established. According to the air volume requirement of the wind branch under different ventilation network conditions, and the ventilation network sensitivity matrix, the optimal adjustable branch set and the adjustable range of air resistance are determined. The grey wolf optimization algorithm is improved by the strategies of initializing the population with good point set, fusing with the differential evolution algorithm and optimizing nonlinear control parameters. Moreover, the improved algorithm is used to optimize the mine air volume regulation model, obtain the maximum adjustable air volume of the wind branch and the corresponding adjustable branch wind resistance. In the context of the actual operation of the mine ventilation network, an intelligent air volume regulation scheme is proposed to select the number of branches according to the branch air volume requirement, and the scheme is verified based on the mine intelligent ventilation experimental platform. The results show that the regulated branch air volume meets the mine safety air volume requirement under the condition of meeting the minimum air volume requirement of other branches.