由于航空事故具有致因机理复杂且致因因素较强的灰色特性，传统的灰色预测模型只适用于单变量预测且具有预测精度低的缺陷。基于此，提出一种以遗传算法优化的多变量灰色模型开展航空安全预测的方法。首先，以航空事故致因理论为基础，从SHEL模型的角度运用鱼骨图确定航空安全影响因素，并以相关系数矩阵可视化图形进一步筛选关键致因因素；其次，构建以人为因素、环境因素、设备设施因素、外来影响因素等为强输入指标的多变量灰色模型，并利用遗传算法对模型的待定参数r全局搜索最优解；最后，以2007-2016年中国民用航空器事故征候万次率和航空不安全事件统计为对象进行仿真实验，并利用GM（1，1）和MGM（1，n）两种灰色预测模型进行预测对比。结果表明：提出的方法与传统的灰色模型相比，在短时间的航空安全预测中平均预测误差约为1.6%，验证了该方法的有效性和较高的预测精度。

Considering that the causal mechanism of aviation accidents is complicated and has many causal factors with strong gray characteristics， the traditional gray prediction model is only applicable to univariate prediction and has the defect of low prediction accuracy.A method of aviation safety prediction was proposed based on a multivariate gray model optimized by genetic algorithm. Firstly， the analysis method of fishbone diagram was applied from the perspective of SHEL model to determine the factors affecting aviation safety， and the correlation coefficient matrix visualization graph was used to further screen the key causative factors. Secondly， a multivariate gray aviation safety prediction model was constructed with human factors， environmental factors， equipment and facility factors， external influencing factors and as the strong input indexes of the prediction model， and the optimal solution of the model’s undetermined parameter r was searched globally and parallel by genetic algorithm. Finally， simulation experiments were conducted utilizing Chinese civil aircraft accident rate of 10 000 and aviation unsafe event statistics from 2007 to 2016. Predictive comparisons were then made between two gray prediction models， GM（1，1）and MGM（1，n）. The findings indicate that compared to the traditional gray model， the proposed method demonstrates an average prediction error of around 1.6% in the aviation safety short-time prediction， showcasing the effectiveness and high accuracy of the proposed method.