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Strong transient aerodynamic loads will produced when a train passing another in high speed. The aerodynamic loads cause transient vehicle lateral vibration, which causes the deterioration of lateral stability. To improve the train running stability, big data method and iterative learning control were used and iterative learning active control algorithm based on high-speed train running big data was proposed. The co-simulations based on multibody dynamics and control algorithm were performed and even researched robustness of control algorithm when the magnitude of aerodynamic loads or intersection time varied. The results show that the big data based iterative learning active control through five iterations can make vehicle lateral vibration peak reduce 52.67% under aerodynamic loads, and control algorithm has strong robustness when intersection conditions change.