The problem of reduced comfort in unmanned vessel rides due to the lack of adaptability to uncertain model parameters and external environmental disturbances is addressed. A trajectory tracking sliding mode control algorithm based on Long Short-Term Memory (LSTM) is proposed. LSTM is utilized to compensate for uncertain model parameters and external environmental disturbances, thereby mitigating the jitter phenomenon in sliding mode control. A mathematical model of an unmanned vessel is established based on a recreational boat, and a sliding mode trajectory tracking controller is designed. Additionally, an LSTM neural network is introduced to compensate for uncertainties in the unmanned vessel's mathematical model and external environmental disturbances. Simulation tests are conducted using MATLAB/Simulink under three different trajectories. The results indicate that the LSTM-based sliding mode control algorithm achieves higher trajectory tracking accuracy compared to the sliding mode control algorithm, with a maximum reduction of 62% in average absolute trajectory error. The LSTM neural network significantly improves the unmanned vessel's disturbance rejection capability.