Quantitative analysis of mixture components by Raman spectroscopy is a difficult problem in analytical chemistry.The existing quantitative analysis methods of the mixture based on machine learning (such as support vector regression, partial least squares) have the problems of difficult to obtain training samples and poor generalization performance of models. A direct quantitative analysis method for mixture with known components based on Raman peak intensity and the least square fitting algorithm was proposed in this study. Firstly, the Raman spectra of the mixture and its components were collected respectively, and the noise reduction and baseline correctionforthe Raman spectra were conducted by using the combination of continuous wavelet transform and penalized least square method. Secondly, the preprocessed spectra were divided into several spectral subintervals by slope comparison method, each subinterval was regarded as the linear superposition of several Voigt functions, and the positions, intensities and half-widths of the peaks were obtained by Levenberg-Marquardt-Fletcher (LMF) algorithm. Thirdly, the contribution value of each component to the spectral peak of the mixture was determined based on the peak position. Finally, the over determined equation was established based on lambert-beer law thatthe peak intensity of mixture is proportional to its concentration of components, the coefficients corresponding to each component were obtained by fitting the equation with the least square method, so that the volume concentration of each component was obtained. In this study, ten kinds of ternary mixtures (9 volume concentration ratios of each ternary mixture) were prepared with 6 components, including ethanol, acetonitrile, acetone, cyclohexane, diacetone alcohol and diethyl malonate, and Raman spectral data of 90 mixtures and 6 components were collected. If the spectra of mixtures and their components were acquired at the same measurement conditions (power and integral time), the obtained correlation coefficient (r) foreach componentwas above 0.96, the root means square error (RMSE) was less than 6%, and the residual prediction deviation (RPD) was greater than 2.5. If the spectra of mixtures and their components were acquired at the different measurement conditions, the correlation coefficient (r) was above 0.93, the RMSE was less than 7.94%, and the RPD was greater than 2.0, this proved that the algorithm has good accuracy and robustness for quantitative analysis of mixture based on Raman spectroscopy. The proposed method can achieve a arapid and accurate direct quantitative analysis of ternary mixtures, which provides an effective way for the quantitative analysis of mixtures.