提出一种基于自适应噪声的完备经验模态分解(CEEMDAN)拉曼光谱去噪方法。利用排列熵(PE)作为代表噪声的内在模式分量(IMF)的判据，并对其进行阈值滤波以消除噪声，得到消除噪声的拉曼光谱重构信号。采用该方法对乙醇水溶液的拉曼光谱数据进行去噪研究。结果表明，该方法有效地消除了拉曼光谱中的噪声；与经验模态分解法(EMD)和总体平均经验模态分解法(EEMD)的对比表明，CEEMDAN 去噪法不但在高信噪比的拉曼光谱信号去噪中取得良好的去噪效果，而且在低信噪比的拉曼光谱信号去噪中依然优势明显。

A Raman spectroscopy denoising method based on complete ensemble empirical mode decomposition with adaptive noise is proposed. Permutation entropy is employed to judge intrinsic mode functions (IMFs), if they are on behalf of noise. The IMFs should be filtered based on threshold value. Then the reconstructed signal is without noise. This method is used to denoise the Raman spectra of ethanol solution. The result shows that the method can effectively remove noise from Raman spectroscopy. Compared with empirical mode decomposition and ensemble empirical mode decomposition, the method does not only achieve better performance at high signal to noise ratio (SNR), but also has obvious superiority at low SNR.