## 5 Conclusions  
In the background of world economic globalization, shipping has become one of the most important modes of transportation in international trade. The number, types, and new routes of ships continue to increase. Although the shipping trade shows a thriving atmosphere, it also makes the channel congested and the load increases, which affects the safety of ship navigation and seriously threatens the life and property safety of ship personnel. From the analysis of the ship accident investigation organization, it can be seen that human error is the main cause of marine and inland river accidents. The key to the safe navigation of ships lies in the perception of the surrounding navigation environment during the navigation process and the effective use of varied information for correct analysis and decision making. As a common navigation environment perception means, AIS has some deficiencies in the process of receiving and sending ship information, which restricts the maneuvering behavior of the ship. Knowing how to use the AIS information to accurately predict the trajectories of their own ship and the target ship in a specific time is vital for a ship driver to make a correct evaluation and decision. In order to improve the prediction accuracy and calculation efficiency when predicting the future position of the ship in a specific period, this paper introduces the LSTM network and the GA optimization algorithm to the future position prediction of the ship and proposes a new method based on GA-LSTM to predict the course and position of inland ships. Considering the disadvantages of AIS equipment in the process of receiving and sending ship information, firstly, the collected real-time AIS data are preprocessed and the cubic spline interpolation method is adopted to interpolate data for the data loss of individual ships. Then, RNNs have powerful time-series processing capabilities, and are able to use historical information to accurately predict future state, building the ship trajectory prediction model based on the LSTM network model, and utilizing the GA optimizes the hyperparameters of the LSTM network. The GA-LSTM prediction model can minimize the impact of hyperparameter factors on the accuracy of ship trajectory prediction. In this paper, two typical ship trajectories in the Wuhan section of the Yangtze River are selected for prediction experiments and compared with the classic SVM method and the LSTM method. The experimental results show that the GA-LSTM model proposed in this paper has higher prediction accuracy and prediction speed; this model not only performs well in predicting the trajectory of the ship sailing in a straight line but also has a strong advantage in trajectory prediction when the ship starts to maneuver or maneuvers at a large angle. On the basis of high computing efficiency, it can predict ship trajectory in real time and accurately provide effective guarantee measures for the safe navigation of ships, and it has better generalization ability. This model solves the problems of low prediction accuracy and complex calculation of ship trajectory prediction to a certain extent and has good practical application value in the intelligent navigation of inland river ships. The GA-LSTM model is similar to other time-series models, and the effect of ship trajectory prediction with long-term information is not ideal. On the basis of ensuring the prediction accuracy and speed, if the accuracy of long-distance ship position prediction can be improved, it will provide great help for ship collision avoidance and other maneuvering processes. Even though the model in this paper is based on a recursive network model, the calculation cost is relatively high in long-distance ship position prediction, and it is not practical in the collision avoidance maneuvering of unmanned ships. How to predict the ship's position without losing prediction accuracy and efficiency and how to decrease the computational cost as well will be carried out in further work. **Author Contributions:** Conceptualization, Y.Z.