Raman spectrum signal is a kind of scattering signal based on molecular vibration. The laser source wavelength of Raman spectrometer is generally nanometer. As it is a typical non-stationary signal and considering the scattering frequency shift, the effective information of Raman spectrum is mainly concentrated in the higher frequency band. Because Raman scattering is very weak, and the signal is easily disturbed by high frequency noise and fluorescence background. In order to obtain more comprehensive Raman information, the signal needs to be processed. The results of Raman signal analysis by wavelet transform depend on the choice of wavelet bases, and the results of different wavelet bases are different; Empirical Mode Decomposition (EMD) method can analyze signals adaptively without setting parameters, but it has the problem of mode mixing. The Ensemble Empirical Mode Decomposition (EEMD) effectively solves the problem of mode mixing in EMD method, and can more clearly divide the components of different frequencies in signals, so it is more suitable for the characteristic analysis and processing of Raman signal which has rich frequency components. In this paper, Raman spectrum of soybean oil, peanut oil, corn oil and sunflower seed oil samples are collected by Raman spectrometer. Raman spectrum of edible oil are adaptively decomposed and processed by EEMD, and a total of 10 orders Intrinsic Mode Function (IMF) are obtained. According to the energy distribution and amplitude characteristics of the signal, IMF1 and IMF2 are characterized as the noise components of the signal, IMF3—IMF7 as the Raman characteristic signal components, the last order IMF10 as the fluorescence background component, and IMF8 and IMF9 as the frequency components of other physical meanings. After filtering out the high frequency noise components of IMF1 and IMF2, it obtains the Raman signal after de-noising. In addition, the signal-to-noise ratio of Raman signal is increased by 2～5 times by enhancing and reconstructing the characteristics of the effective signal component. Among them, the dynamic peak at 1 745 cm-1 caused by the ester bond carbonyl stretching vibration is significantly enhanced, which is difficult to detect. Finally, the baseline of original signal and the characteristicenhancing signal are deducted by PLS method based on continuous wavelet transform. After principal component analysis, the different data samples without enhancement overlap with each other, and there is no obvious class spacing, so it is difficult to distinguish the type of samples completely. The data samples based on feature enhancement are gathered separately, and each kind of data samples is clustered obviously. Types can be identified from each other, which provides a new way for Raman spectroscopic signal processing.