In this study, based on Mel frequency cepstrum coefficient (MFCC) method, the AE signal characteristics of coal and rock samples were extracted, and the stress state criterion based on signal features was constructed. By integrating back propagation (BP) neural network for deep learning of signal characteristics, the recognition, classification, and prediction of coal and rock materials were realized. The results show that the MFCC could characterize the variation law of the original signal, with the sharp fluctuation of the amplitudes of both the AE signal and MFCC when the rock stress was near the peak value. Considering the ratio of sample stress to peak stress as the stress state, the correlation between MFCC and stress state was analyzed. The BP neural network exhibited a high accuracy rate for the signal characteristics represented by MFCC, achieving an accuracy of more than 95% with a fast recognition speed. Notably, the evaluation results of neural network model were stable and reliable. Therefore, MFCC can be used to extract the AE waveform signal characteristics and evaluate the stability of stress state for coal and rock materials. The recognition, classification, and prediction of high-precision results of the two types of waveform characteristics of coal and rock can be achieved through BP neural network.

本研究基于梅尔频率倒谱系数（MFCC）方法，提取煤岩样品的声发射信号特征，构建了基于信号特征的应力状态判据。通过集成反向传播（BP）神经网络对信号特征进行深度学习，实现对煤岩材料的识别、分类和预测。结果表明，MFCC能够表征原始信号的变化规律，当岩石应力接近峰值时，AE信号和MFCC的幅值均出现剧烈波动。以试样应力与峰值应力的比值作为应力状态，分析了MFCC与应力状态的相关性。BP神经网络对以MFCC为代表的信号特征表现出较高的准确率，识别速度快，准确率达95%以上。值得注意的是，神经网络模型的评价结果​​稳定可靠。因此，MFCC可用于提取声发射波形信号特征，评价煤岩材料应力状态的稳定性。通过BP神经网络可以实现对煤岩两类波形特征的高精度结果的识别、分类和预测。