## 1 Introduction
Subsequently, Si-PLS wavelength selection is applied to further extract optimal spectral bands of the IMF layers, and singular entropy is obtained through singular value decomposition to establish a multivariate stepwise regression prediction model. The above processing based on VMD targets near-infrared spectra, rather than Raman spectra. Near-infrared spectra have smooth characteristic bands and less noise interference, with valid feature information concentrated in the low frequencies. Thus, near-infrared spectral denoising should focus on removing high-frequency noise. In contrast, Raman spectra have more useful information in high or even extremely high frequencies due to their sharp characteristic peaks. Raman spectral denoising should pay more attention to separating feature information from noise in the high frequencies. The high-frequency characteristic information of Raman spectra is often mixed with high-frequency noise. Operating on a low-frequency IMF can easily lead to the loss of high-frequency charac teristic information and signal distortion, failing to obtain favorable experimental results. With the development of intelligent optimization algorithms, [PERSON] et al. [16] searched for the optimal solution for the two parameters of VMD using the fruit fly optimization algorithm (FOA) and selected a corresponding fitness function as the optimization indicator. However, genetic optimization algorithms are relatively complex, with slower iteration and solution processes. In summary, some common issues in Raman spectral denoising are listed as follows:  
* Mode mixing problem: The use of empirical mode decomposition results in mode mixing problems, where high-amplitude noise cannot be differentiated from high-frequency Raman signals. The variational mode decomposition method can resolve the issue of mode mixing in Raman spectral lines, but the residual sequence after VMD decomposition still has complex high-frequency variations that cannot be effectively utilized. * Complicated and low efficiency: The use of swarm intelligence algorithms, such as genetic algorithms and fruit fly optimization, relies on complex iterative optimization algorithms for parameter tuning, which is time consuming due to the extensive iterations for optimization and the algorithms are difficult to construct. * Lack of intelligent neural network algorithms: The goal of Raman spectral line denoising is to improve the quality of Raman spectral curves. Therefore, the noise residual between the original signal and the reconstructed spectral curve contains high-amplitude noise and high-frequency Raman signals, which can lead to bias errors in noise estimation. Currently, there are scarce practical applications of neural network models for Raman spectral denoising. To address the aforementioned issues, this study investigates variational mode decomposition (VMD), empirical wavelet transform (EWT), and integrated encoder-bidirectional long short-term memory (EBiLSTM) modules to propose a neural network algorithm for adaptive denoising of Raman spectra. This enhances the denoising capabilities, while greatly simplifying the complex optimization of the parameters, making it more suitable for automated implementation. In summary, the main contributions from this work are:  
* To address the mode mixing problem in EMD decomposition and the complexity of decomposing high-frequency features in VMD, VMD is utilized to decompose the original load signal into several sub-sequences and a residual sequence, resolving the signal mode mixing issue. EWT is then leveraged to further decompose the residual sequence, significantly reducing the complexity of the original Raman spectral lines. The correlation coefficient between each modal component and the original signal is computed, along with the zero-crossing rate index, to categorize the noise and signal sequences.