Trajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.

轨迹数据可以客观地反映出运动物体的运动规律。因此，轨迹预测具有较高的应用价值。飓风经常造成无法估量的生命和财产损失，轨迹预测可以成为减轻飓风造成的破坏的有效手段。随着人工智能技术的普及和广泛应​​用，从机器学习的角度出发，本文通过基于长期短时记忆（LSTM）网络的历史轨迹数据来训练轨迹预测模型。提出了一种改进的LSTM（ILSTM）轨迹预测算法，改进了对简单LSTM的预测，并使用卡尔曼滤波器对改进的LSTM算法的预测结果进行滤波，称为LSTM-KF。