Gale weather can easily cause highspeed train accidents such as derailment and rollover. Therefore, the ultra shortterm prediction of wind speed is of great significance for the safe operation of highspeed rail. A prediction model based on long shortterm memory (LSTM) networks is proposed in this paper. The maximum wind speed data per minute collected by WindLog wind speed sensor is preprocessed. The proposed model was trained using wind speed data of Haidian District, and the wind speeds 1, 5 and 10 min ahead were predicted. The mean absolute error (MAE) of 1min ahead prediction was 0467 m/s with the accuracy rate of 100%. The MAE of 5 min ahead prediction is 0543 m/s with the accuracy rate of 996%, the MAE of 10 min ahead prediction is 0627 m/s, and the accuracy rate was 988%. The experimental results show that the prediction model has better adaptability and higher prediction accuracy.