###### Abstract  
To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing. acoustic emission signal recognition; improve CELMD; correlation coefficient; Swin Transformer neural network +
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methods to extract key features [12]. Wavelet transform is capable of capturing the transient characteristics of signals but requires appropriate selection of wavelet basis functions and scales [13]. Fourier transform is widely used for frequency domain analysis of acoustic emission signals but is less effective in capturing transient events compared to wavelet transform [14]. Furthermore, Fourier transform is prone to boundary effects when analyzing signals of finite duration [15]. EMD (Empirical Mode Decomposition) decomposes signals into a series of IMFs (Intrinsic Mode Functions), but during the decomposition process, modal overlap may occur due to interference between different modes [16]. Similarly, if the starting or ending parts of the signal are not recorded, errors in decomposition, known as endpoint effects, can occur [17]. LMD (Local Mean Decomposition), proposed by [PERSON] and others, decomposes signals into multiple product function components with physical significance, alleviating mode mixing and endpoint effects [18]. However, LMD is prone to errors due to noise interference during the decomposition process [19]. ELMD (Enhanced LMD) and CELMD (Complex Enhanced LMD) optimization methods introduce white noise into the original signal to decompose signals into more suitable frequency bands, albeit with varying degrees of residual white noise [20]. CELMDAN (Complex Enhanced LMD with Adaptive Noise) further reduces the impact of noise on decomposition by significantly increasing the number of iterations, but it suffers from the issue of high computational complexity [21]. The method of identification and classification of acoustic emission signals has a significant impact on classification accuracy [22]. Traditional methods primarily rely on manual identification, with researchers utilizing the spectral features of signals and employing methods such as fractal theory, chaos models, and others to assist with signal identification [23]. However, the accuracy of these methods is limited by the experience and expertise of the operator, resulting in generally lower accuracy. In recent years, the development of deep learning has provided new possibilities for the classification of acoustic emission signals. Researchers applied BP neural networks to the classification of acoustic emission signals [24], while other researchers introduced CNN (convolutional neural network), with both achieving favorable recognition rates [25]. Transformer is a neural network model proposed by Google based on self-attention mechanisms, which has achieved great success in natural language processing [26].