Abstract: Through the time series analysis of gale speed data, this paper established gale speed prediction model to implement the early warning of gale disaster, which was of great significance to improve the operation safety supportability of high-speed railway. The paper analyzed the historical gale speed data of the disaster prevention system of a high-speed railway passenger dedicated line, established a gale prediction model based on LSTM neural network, used TensorFlow platform to train the model parameters, and verified the model with the actual monitoring data. The results show that this method has a good effect in predicting the gale in the next 20 minutes, and the average error of 20 ~ 30 m / s gale prediction is 13.4%. The research can provide reference for the application of high-speed railway gale early warning technology.