目的 探究心率变异性（heart rate variability， HRV）对噪声烦恼的预测效果，构建一种对噪声烦恼进行识别评估的模型。 方法 以在职地铁司机群体为被试者，基于地铁模拟器设计了列车司机驾驶实验，采集了40名在职地铁司机在不同噪声环境下的Weinstein噪声敏感性量表与主观噪声烦恼问卷以及心电数据，对HRV特征进行提取并采用Z-Score标准化将数据转化为标准正态分布。特征选择采用随机森林（random forest， RF）对特征值进行重要度排序依次输入挑选最重要特征，建立了多种基于心率变异性特征的司机噪声烦恼识别模型进行比较，并讨论个体噪声敏感性对准确性的影响。 结果 多种HRV特征与噪声烦恼相关。经特征选择发现，个体噪声敏感性对识别和检测噪声烦恼有显著作用。对比多种分类模型，使用卷积神经网络模型（convolutional neural network， CNN）对烦恼水平进行识别效果最好，准确率为90.03 %。 结论 基于心率变异性的深度学习模型具有良好的识别效果，为实时识别职业噪声烦恼提供了方法和理论支撑。

Objective To explore the predictive effect of heart rate variability （HRV） on noise annoyance and develop a model for identifying and assessing noise annoyance. Methods A group of employed subway drivers participated in a simulated train driving experiment under different noise conditions. The Weinstein Noise Sensitivity Scale， subjective noise annoyance questionnaire， and electrocardiogram data were collected. HRV features were extracted and transformed into a standard normal distribution using Z-Score normalization. Random Forest （RF） was used for feature selection and important features were inputted to establish various driver noise annoyance identification models based on HRV features. The impact of individual noise sensitivity on accuracy was also discussed. Results Multiple HRV features were found to be related to noise annoyance. Feature selection revealed that individual noise sensitivity significantly influenced the identification and detection of noise annoyance. Among various classification models， the Convolutional Neural Network （CNN） model achieved the best performance in identifying annoyance levels， with an accuracy of 90.03%. Conclusion The deep learning model based on HRV demonstrated excellent performance in identifying noise annoyance， providing a method and theoretical support for real-time recognition of occupational noise annoyance.