Aiming at the problem that the traditional recurrent neural network (RNN) model has slow convergence speed and low accuracy, resulting in a large difference between the predicted trajectory and the real trajectory of a maritime ship, an Seq2Seq (sequence to sequence) model composed of RNNs is constructed. Attention mechanism and convolutional neural network (CNN) are introduced to improve the model, strengthening the ability of extracting data features, accelerating the convergence speed of the model, and improving the trajectory prediction accuracy. The experimental results show that：compared with the traditional RNN model, the mean square error, the root mean square error, and the average absolute error of the Seq2Seq model are reduced by 81.41%, 12.67%, and 62.43%, respectively；compared with the Seq2Seq model, the mean square error, the root mean square error, and the average absolute error of the improved Seq2Seq model are reduced by 42.87%, 69.27%, and 45.79%, respectively.