准确的高铁沿线风速预测是铁路灾害预警系统的基础需求，为了提升应对和处理强风灾害致突发事件的能力，提出一种基于减法平均优化（subtraction average based optimizer, SABO）算法优化长短时记忆（long short-term memory, LSTM）神经网络的高铁沿线短期风速预测方法。首先，针对风速非线性和非平稳特性，采用极小化极大（min-max, MM）方法对风速数据进行归一化处理；其次，采用SABO算法中的“- v”方法对LSTM模型的关键参数搜索寻优，并构建风速预测模型；最后，以中国宝兰高铁沿线风速采集点采集的实测风速数据为例，对模型进行有效性检验。实验结果表明：SABO算法的寻优效果更加良好，预测精度更高，所建模型的平均绝对误差（mean absolute error, MAE）、平均绝对百分比误差（mean absolute percentage error, MAPE）和均方根误差（route mean square error, RMSE）分别仅为11.96%、1.23%和16.47%，决定系数（r-square，R²）为0.995。与其他模型相比，通过SABO算法优化后的LSTM神经网络在短期风速预测上具有较好的拟合效果和更高的预测精度，可为高铁沿线大风预测预警提供一种新的方法和思路。

Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the "- v" method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm's optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R²) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.