Raman spectra are scattering spectra based on the Raman scattering effect. Since the vibration and rotation energy characteristics of different kinds of substances are unique, the resulting Raman scattering spectra are also unique. Raman spectroscopy is very advantageous in identifying the composition of substances. It is also favored for its lossless, non-contact, fast, simple, and repeatable characteristics and is widely used in various fields such as chemistry, physics, biology, and medicine. However, due to the weak signals measured, the processing accuracy of optical instruments, and the interaction between the components of the mixture, the Raman spectra of the mixture not only have the phenomenon of overlapping peaks but also some of the characteristic peaks of the weaker signals may be submerged in the background noise, which affects the accuracy of Raman spectroscopy analysis of mixtures. This study applies principal component analysis to Raman spectral analysis to solve the difficulty of analyzing and identifying the weak signals in Raman spectra. It proposes a Raman signal extraction method based on full spectral information. In this method, the measured Raman spectra are regarded as the linear superposition of the spectra of different material components, and the Raman signals of different material components are extracted through the principal component analysis of multiple Raman spectra with different component ratios, separating the background noise and random noise. According to the characteristics of Raman spectra of material components, which are not necessary to satisfy orthogonality, this paper analyzes and discusses the relationship between spectral principal components and Raman spectral components of material components and gives a general method of using the spectral principal components to be corrected to Raman spectra of material components. In addition, according to the linear correlation characteristics between the spectral principal components and the concentration of the material components, this paper also gives the basis for determining the Raman spectra of the material components, the linearity error, and the random noise. Through the experimental verification of Raman spectra of methanol and ethanol mixed solutions with different concentrations, the extraction of methanol and ethanol Raman signals is realized. The background noise and random noise are successfully separated. The final results match the reference signal well. The judgment results of Raman signals of the material components, linearity error, and random noise are verified simultaneously. In this paper, an effective method of extracting actual spectral components using Raman spectral principal components is proposed, which has the advantages of being fast and convenient, low cost and high accuracy, and is a useful supplement and attempt to the Raman spectral data processing technology, and has great potential for application in substance identification and concentration detection.