针对船舶轨迹预测精确性与实时性的需求，从数据层面探究影响船舶航行轨迹的特征，通过相关性分析确定网络的输入，提出结合循环神经网络-长短期记忆（Recurrent Neural Networks - Long Short Term Memory，RNN-LSTM）的船舶航行轨迹预测模型。通过船舶Z形试验相关数据与实船实际航行数据对网络模型进行训练，并对未来船舶航行轨迹进行预测。对未来轨迹的预测值与实际值进行对比。结果表明，模型预测误差小，验证该方案在船舶轨迹预测中的实用性和有效性。
In terms of the accuracy and real-time requirements of ship trajectory prediction, the characteristics influencing the ship navigation trajectory are explored at the data level, the network input is determined through the correlation analysis, and a ship navigation trajectory prediction model combining the Recurrent Neural Networks - Long Short Term Memory (RNN-LSTM) is proposed. The network model is trained through the relevant data of ship Z-shape test and actual ship navigation data, and the prediction of future ship navigation trajectory is made. The prediction value of future trajectory is compared with the actual value. The results show that the error of model prediction is small, which verifies the practicability and effectiveness of the scheme in the ship trajectory prediction.