When a high-speed train passes through a long tunnel, the tunnel pressure wave induces the interior pressure fluctuation through the gaps, the air ducts and the car body deformation. The traditional passive control algorithm of closing the air ducts for a fixed period may fail to meet the air pressure comfort and the fresh air amount inside the vehicle at the same time, so a novel active control algorithm needs to be established. Firstly, a multi-factor coupling transfer process of internal and external air pressure is studied and modelled, which takes the nonlinear characteristics of the gaps, the air ducts and the car body deformation into consideration. Then, the threshold iterative learning control algorithm (TILC) of interior pressure fluctuation is designed, in which the opening and closing time of the air ducts matches the characteristics of the interior air pressure. The simulation results show that the threshold iterative learning control matches the opening and closing time of the air ducts with the characteristics of the interior air pressure. Besides, requirements for a better riding comfort and a reasonable air quality will be achieved with TILC applied, by comparing with the performances in the uncontrolled condition and under the traditional  passive control algorithm.