Crosswind is a critical environmental factor affecting the dynamic interaction between the pantograph and catenary in high-speed trains, which can severely compromise the operational stability of the system. To address this challenge, this study develops an active pantograph control scheme for crosswind disturbances by employing a neural network-based PID controller. First, the target value is determined based on the train operating speed and inherent data of the pantograph-catenary system, and a PID controller is constructed. Subsequently, a neural network is integrated into the controller to train the system's output contact force and PID parameters using its nonlinear approximation capability, thereby optimizing the parameters and achieving effective control of the system. The effectiveness of the controller is then validated by applying the proposed method to a high-speed train pantograph-catenary system under crosswind conditions, with its control performance thoroughly analyzed. The results indicate that the proposed control scheme demonstrates effective regulation of the pantograph-catenary system across various typical crosswind scenarios, achieving significant reduction or even complete elimination of pantograph-catenary's contact loss rate while exhibiting strong robustness, thereby proving fully applicable for practical implementation in high-speed railway engineering applications.