This paper addresses the control problem of uncertain systems subject to external disturbances, unknown nonlinearities, and input saturation constraints in ship heading control. A nonlinear regulation control algorithm is designed that integrates adaptive neural networks with dynamic surface control (DSC) technology. The algorithm employs a radial basis function (RBF) neural network to approximate external disturbances and unknown nonlinear functions, while the integration of DSC technology effectively reduces computational complexity. A nonlinear function featuring error-dependent gain characteristics is incorporated into the control law design. This adaptive nonlinear control approach effectively eliminates potential singularity issues, while an auxiliary system is designed to compensate for the effects of input saturation constraints. Using Lyapunov stability theory, the uniform ultimate boundedness of all closed-loop signals is rigorously proven. The proposed algorithm is validated through MATLAB simulations of ship heading tracking control under input saturation constraints. Comparative experimental results demonstrate the superior performance and advantages of the proposed method. The findings provide both theoretical foundations and practical references for related fields, demonstrating significant application value in ship engineering practice.