An adaptive fixed-time sliding mode control method is proposed for the unmanned helicopter system with external disturbances and input saturation. A segmented function is selected to ensure that the sliding mode variables are continuously differentiable and eliminate the singularity problem of the controller. The radial basis function neural networks are used to estimate and compensate for external disturbances and input saturation errors. To improve the tracking performance, the fixed-time control laws are developed to guarantee that tracking errors converge to a small neighborhood of the origin within a fixed time, which can enhance the convergence speed and tracking accuracy of the helicopter system. Finally, the simulation results based on an unmanned helicopter show the effectiveness and superiority of the proposed control method.