To address the issue of poor dynamic performance in control systems caused by the output uncertainty of insufficiently trained Jordan neural network (JNN), a novel position compensation control method of maglev ball based on fuzzy inference (FI) and JNN was proposed. A three-module control framework was designed, consisting of a basic control module, a JNN control module, and a FI module. The basic control module adopted a highly adaptable PID controller to provide baseline control performance. The JNN control module performed real-time identification and compensation for the maglev ball system. The FI module dynamically adjusted the output of the neural network controller to suppress the uncertainty introduced by insufficiently trained JNN. The experimental results show that，compared with the traditional neural network compensation control method, the proposed method reduces overshoot by 39.79% and 60.61% and shortens the settling time by 19.52% and 48.47% when tracking step and square wave signals. The proposed method significantly enhances the dynamic performance of the control system while maintaining steady-state accuracy.