To improve the operational stability of high-speed maglev trains and their ability to respond to disturbance excitations, this paper designs a control algorithm based on fuzzy neural networks and backstepping control theory. First, a numerical model of the high-speed maglev vehicle-guideway coupled system was established, and the vehicle-guideway coupled vibrations were solved using an implicit integration method. Subsequently, a backstepping control algorithm was designed for the model and combined with a fuzzy neural network to form a backstepping fuzzy neural network control algorithm. The reliability of the model was then demonstrated using measured data. Finally, by comparing the dynamic responses of the maglev train under two control algorithms to irregularity and misalignment excitations, it was confirmed that the backstepping fuzzy neural network control algorithm has superior control performance and robustness.