The utilization of multi-stage fan station ventilation technology is crucial in the ventilation system of non-coal mines，particularly as mining depths increase.Traditional large main fan ventilation methods may struggle to adequately meet the ventilation requirements of deep mining operations，highlighting the significance of this technology.The multi-stage fan station ventilation system allows for the precise control of air volume and pressure in individual partitions by adjusting the operational status of fans at each level of the station.This method enhances the precision of adjustment and flexibility of control in the ventilation system，increases its efficiency，and decreases energy consumption.The conventional fan optimization approach assumes that each circuit accommodates only one air volume branch.In contrast，the ventilation network solution method utilizes the fan as a residual tree branch，enabling the direct allocation of unbalanced air pressure （fan pressure） to the residual tree branches of each autonomous circuit for fan optimization.In a multi-fan multi-stage station ventilation system，a single circuit may contain multiple installed air volume branches，rendering the traditional fan optimization method ineffective.Therefore，it is necessary to develop a fan optimization method suitable to multi-stage fan stations.Furthermore，due to the mutual influence between the installed branches in the multi-fan multi-stage fan station ventilation fan system，it is necessary to further consider the logical problem of air volume between the installed branches and the optimal allocation of unbalanced wind pressure（fan pressure）.In order to determine the optimal unbalanced wind pressure distribution method for multi-stage fan station，this study proposed a multi-objective optimization model for fan selection of multi-stage fan stations，aimed at achieving the minimum fan power consumption while optimizing the fan air volume and pressure.The proposed model assists in determining the most suitable fan model and installation angle for each installation point.The model successfully achieves the optimal solution that closely aligns with the air volume requirements，while also circumventing the issue of non-convergence in nonlinear model solutions.Additionally，it demonstrates efficient solution capabilities in large-scale，multi-stage fan station ventilation systems.Furthermore，the multi-fan optimization method proposed in this study establishes a mutual constraint relationship between stations at each level using a mathematical model.This approach ensures that the unbalanced wind pressure at each installation point remains within the operational range of the fan，thereby addressing the issue of distributing fan pressure effectively across all levels of the station and preventing fan selection failure due to improper pressure distribution.The reliability of the fan selection scheme for the multi-stage fan station ventilation system is confirmed through example verification in this research.