中文摘要：

从研究人的不安全行为本身出发,将设计与使用、管理、行为失误这3个方面作为构建矿工不安全行为指标体系的影响因素。结合专家咨询和煤矿实际调研情况,尝试使用遗传算法优化神经网络,构建基于矿工不安全行为的煤矿安全预测评价模型。选取山东、河南等地大型煤矿的实时监测数据进行样本学习及实证分析。实例检验表明：安全预测评价模型短期的预测结果与实际情况相符合,能够提前对煤矿安全状况进行较为准确的预测。使用遗传神经网络算法实时的预测煤矿整体安全状况,通过反馈的结果反向作用于煤矿的安全管理决策,有利于为管理者提供决策支持。

英文摘要：

This paper conducts a study on the issue of human unsafe behavior and constructs miners＇ unsafe behavior indicators system from three aspects of design and use, management and behavioral mistakes. Combined with the expert consultation and the actual situation of coal mine, this paper tries to use the genetic algorithm to optimize neural network and constructs a coal mine safety prediction and evaluation model based on miners＇ unsafe behavior. Sample learning and empirical analysis is made based on real-time monitoring data collected from large-scale coal mines in Shandong, Henan and some other places. The results show that a short-term prediction results is consistent with the actual situation and able to predict the coal mine safety situation in advance.It is feasible to use genetic neural network algorithm to predict the overall safety of coal mine in real time and the feedback of results may provide decision support for managers for sure.