为克服地铁车站火灾安全指标动态性变化、耦合性作用等特征导致管理人员难以借助传统数理统计方法对车站安全做出科学且具有延展性评价的难题，提出构建基于C-OWA算子和BP神经网络的地铁车站火灾安全评价模型。首先，根据火灾发生的不同阶段并考虑人员疏散因素，构建地铁车站火灾安全评价指标体系； 然后，利用C-OWA算子对专家决策数据重新集结处理，削弱个别极值对权重的影响，提高赋权的科学性； 最后，利用BP神经网络连续自适应学习和反馈能力模拟指标复杂的耦合关系。将该模型运用在郑州地铁2号线紫荆山车站的火灾安全评价中，结果表明该车站火灾安全性高。

It is difficult for managers to make scientific and extensible evaluation on metro station safety based on traditional mathematical statistical method induced by dynamic variation of fire safety indices of metro stations and coupling effect. Hence, a safety evaluation model for metro station in case of fire based on COWA operator and BP neural network is proposed. Firstly, a fire safety evaluation index system for metro station is established from the aspect of different stages of fire and considering the factors of personnel evacuation. And then the COWA operator is used to regroup the decisionmaking data of experts to weaken the influence of individual extreme value on the weight and improve the scientificity of empowerment. Finally, [JP2]the continuous adaptive learning and feedback capabilities of BP neural network are used to simulate the complex coupling of indices. The model is applied to the evaluation of fire safety of Zijinshan Station on Line 2 of Zhengzhou Metro; and the results show that the fire safety level of the station is high.