To deal with the problem of path following for 4 degree-of-freedom (4-DOF) unmanned surface vehicles in the presence of dynamic uncertainties and external disturbances, an adaptive neural network path following control method based on the finite-time integral line-of-sight (FT-ILOS) guidance is proposed. Under the line-of-sight (LOS) guidance frame, the finite-time theory is utilized, and the integral mechanism and a novel guidance mechanism are introduced, achieving finite-time convergence of the ships' position tracking error while avoiding the saturation risk associated with the guidance integral term. On the basis of a backstepping control design framework in conjunction with FT-ILOS guidance method, the method employs adaptive neural networks to approximate compound disturbance terms and adopts virtual parameter learning techniques to address the "curse of dimensionality" problem, while also applying dynamic surface control techniques to reduce computational complexity. A periodic event-triggered protocol is established between the controller and the actuator to reduce the actuator response frequency and wear. The boundedness of all signals in the closed-loop control system is proven through Lyapunov stability analysis, and the effectiveness and robustness of the proposed control method are verified through simulation contrast experiments by MATLAB.