长短期记忆网络（long short-term memory, LSTM）模型在开展民航风切变风险预测应用时，存在性能受超参数配置影响大、调优过程复杂耗时等问题。为提高民航风切变风险预测模型的预测性能，提升其适用性和预测精度，提出一种引入精英主义的遗传算法（elite genetic algorithm, eGA）优化LSTM民航风切变风险预测模型，即eGA-LSTM。首先，利用LSTM的长期记忆能力捕捉风切变的时序特征；其次，通过引入精英主义的遗传算法来优化LSTM模型的超参数，确保优秀特性能够被保留并有效传承，从而避免传统遗传算法在迭代过程中可能出现的优秀个体丢失问题，加速超参数优化流程，提升LSTM泛化能力；再次，统计民航历史风切变不安全事件信息，计算其64个月月风切变风险值，划分训练集和测试集开展模型验证工作，并与单纯的LSTM模型作对比。结果表明，在风切变风险预测中，eGA-LSTM模型均方误差（mean square error, MSE）相较于单纯的LSTM模型降低了15.49%，精度上有显著的提升，模型较好地拟合了月风切变风险的历史序列，在风切变风险预测预警方面展现出更高的准确率和可靠性。

When using long short-term memory (LSTM) models for predicting civil aviation wind shear risks, there are issues such as performance being greatly affected by hyper parameter configuration, and the optimization process being complex and time-consuming. To improve the predictive performance, applicability, and accuracy of civil aviation wind shear risk prediction models, an elite genetic algorithm (eGA) optimized LSTM civil aviation wind shear risk prediction model, namely eGA-LSTM, was proposed. Firstly, the long-term memory capability of LSTM was used to capture the temporal characteristics of wind shear. Secondly, elitist genetic algorithms were introduced to optimize the hyperparameters of the LSTM model, which ensures that excellent characteristics can be preserved and effectively inherited, thereby avoiding the problem of losing excellent individuals that may occur in the iterative process of traditional genetic algorithms, accelerating the hyperparameter optimization process, and improving the generalization ability of LSTM. Historical information on wind shear unsafe events in civil aviation were gathered. Wind shear risk value for 64 months were calculated. Data were divided into training and testing sets for model validation, and compared with a simple LSTM model. The results show that in wind shear risk prediction, the mean square error (MSE) of the eGA LSTM model decreases by 15.49% compared to the simple LSTM model, and there is a significant improvement in accuracy. The model fits the historical sequence of monthly wind shear risk well, demonstrating higher accuracy and reliability in wind shear risk prediction and warning.