煤矿突发事件往往会引发矿工的情绪波动，导致其在行为决策过程中产生非理性行为决策倾向，从而显著增加事故发生的风险。近年来，人为因素已成为煤矿事故频发的重要诱因，据中国煤炭工业协会《2024年全国煤矿安全生产形势分析报告》统计，2023年全国煤矿生产安全事故中，由人为因素直接或间接导致的事故占比高达78.5%，其中情绪状态对矿工行为决策的影响尤为显著。基于此，本研究综合运用认知神经科学、心理学与机器学习算法等多学科方法，探究煤矿突发事件下情绪对矿工行为决策影响机制。首先，通过结合煤矿应急决策的情境特征，提出情绪引导型认知-行为模型，并构建煤矿突发事件下情绪对矿工行为决策影响的中介调节模型，通过问卷量表评估矿工的情绪状态、安全认知能力、安全氛围与决策水平，对各个效应进行检验；其次，结合眼动追踪与脑电（EEG）实验，获取相应的行为与生理数据；最后，采用机器学习算法对情绪特征进行识别与分类，从而实现对矿工行为决策的准确预警。本研究不仅丰富了煤矿安全管理中情绪影响决策的理论框架，也为事故风险防控与矿工安全决策提供了切实可行的技术支持。本文主要研究内容和结论如下：

（1）构建了煤矿突发事件下情绪对矿工行为决策影响的中介调节模型，从理论层面揭示了情绪对行为决策的影响机制。该模型分析了在煤矿突发事件下，情绪对行为决策的直接效应、安全认知能力的中介效应和安全氛围的调节效应。研究结果显示，趋近动机情绪对行为决策有显著正向效应，且低强度趋近情绪对行为决策的正向促进尤为突出；而回避动机情绪则呈现负向影响。安全认知能力在情绪影响决策过程中发挥着关键中介作用，其中自我风险感知、对他人风险的感知、安全遵守与意识和自我控制与适应均为关键中介因子。良好的安全氛围能够显著增强情绪对决策影响的正向效应。该结果为不仅厘清了相关变量之间的关系，还为后续实验部分的设计和实施奠定了理论基础。

（2）开展了煤矿突发事件下情绪对矿工行为决策影响的眼动实验，从心理层面揭示了情绪对行为决策的影响。通过采集分析注视时间、注视频率、瞳孔直径变化以及决策参与度等眼动及行为指标，发现在动机方向维度上，趋近动机显著延长矿工的注视时间，使其更全面地获取和处理现场环境信息；而回避动机则明显缩短注视时长，加速信息扫描与决策启动。在动机强度维度上，高强度情绪状态下矿工表现出更高的注视频率，揭示其在剧烈情绪驱动下启动的防御性信息处理机制。该实验不仅从心理认知层面实证了情绪对决策的影响，也为后续神经机制研究提供了关键的行为学依据。

（3）实施了煤矿突发事件下情绪对矿工行为决策影响的脑电实验，从大脑神经层面揭示了情绪对行为决策的影响。通过对N1、P200、N2、P300以及LPP成分进行分析，发现低强度动机状态下N1波幅显著增强，表明矿工对突发事件刺激的初级感知敏锐度提升；高强度动机则激活P2波幅，反映出早期情绪性刺激评价所需认知资源的增加；趋近动机显著提高N2波幅，指示在处理与目标信息冲突时需投入更多认知控制；回避动机则显著增大P300与LPP波幅，并延长ERP潜伏期，揭示面对威胁性情境时更为深入的情绪评估与更高的认知负荷。该研究结果为构建基于神经生理指标的矿工情绪特征识别分类模型和行为决策倾向预警奠定了基础。

（4）构建了基于CNN-Bi-LSTM算法的矿工情绪特征识别分类模型，并提出了结合中心极限定理的行为决策预警方法。该情绪识别模型在情绪特征数据集上实现了高达93.67%的分类准确率；在公开的DEAP数据集上各类情绪识别准确率均超过88%，且能够发出预警信号。研究结果表明该方法能够实时监测矿工的情绪动态并即时输出预警信号，可为煤矿安全管理提供精准、高效的决策支持。

综上所述，本研究从理论建模、心理认知和大脑神经三个层面系统阐明了煤矿突发事件中情绪对矿工行为决策的作用机理。实证结果表明：中介调节模型能够揭示情绪、认知与安全氛围的交互效应；眼动指标验证了情绪动机对信息获取深度与处理方式的影响；ERP成分分析揭示了不同情绪状态下大脑对突发刺激的分阶段加工特点；基于CNN-Bi-LSTM的情绪识别与预警方法则实现了对矿工行为决策的实时监测与预警。该研究不仅丰富了煤矿应急管理中情绪决策机制的理论框架，更为构建高效的应急决策支持系统提供了实用技术路径。

Sudden coal-mine incidents often trigger emotional fluctuations in miners, leading to irrational decision-making tendencies that significantly increase accident risk.According to the 2024 National Coal Mine Safety Production Situation Analysis Report by the China National Coal Association,78.5% of coal-mine safety accidents in 2023 were directly or indirectly caused by human factors, with emotional states exerting a particularly pronounced influence on miners’ decision processes.In response, this study integrates methods from cognitive neuroscience, psychology, and machine learning to develop a mediated-moderation model of emotional impact on miners’ decision-making during sudden coal-mine incidents.First, miners’ emotional states, safety-cognition abilities, perceived safety climate,and decision levels were assessed via standardized questionnaires.Second, behavioral and physiological data were collected through eye-tracking and electroencephalography (EEG) experiments.Finally,machine-learning algorithms were employed to identify and classify emotional features,enabling real-time early warning of irrational decision tendencies.The major research contents and conclusions are as follows:

A mediated-moderation framework was constructed, revealing that approach-motivation emotions exert a significant positive effect on decision-making (with low-intensity approach emotions showing the strongest facilitation), whereas avoidance-motivation emotions have a negative effect. Safety-cognition ability mediates the emotion–decision relationship through key factors-self-risk perception, perception of others’ risk, safety compliance and awareness, and self-control and adaptation-while a positive safety climate amplifies the beneficial effects of emotion on decisions.

In the approach-avoidance dimension, approach motivation significantly prolonged fixation time,allowing miners to gather and process environmental information more comprehensively;avoidance motivation shortened fixation time, accelerating scanning and decision initiation.Under high-intensity emotions, fixation rate increased, indicating a defensive information-processing mechanism.

Analysis of ERP components (N1, P200, N2, P300, and LPP) showed that low-intensity motivation enhanced N1 amplitude (heightened early perception), high-intensity motivation increased P200 (greater cognitive resources for early emotional evaluation), approach motivation elevated N2 (enhanced conflict monitoring), and avoidance motivation augmented P300 and LPP amplitudes and prolonged latencies (deeper emotional evaluation and higher cognitive load).

A CNN-Bi-LSTM model was developed for emotion-feature classification,achieving 93.67% accuracy on a custom dataset and over 88% accuracy across all emotion categories on the DEAP public dataset.Combined with the central limit theorem, this approach provides real-time monitoring of miners’ emotional dynamics and issues prompt warnings of irrational decision tendencies.

Overall,this multi-level empirical research-from theoretical modeling to psychological and neurophysiological experiments-advances the theoretical framework of emotion-driven decision mechanisms in coal-mine safety management and offers robust technical pathways for constructing efficient emergency decision-support systems.

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