Purple rice is a common ingredient in life and has rich nutritional value. Due to the high price of purple rice, the dyed purple rice has entered the market in large quantities. In this paper, terahertz time-domain spectroscopy combined with chemometric methods is used to explore the rapid detection method of purple rice adulteration. The spectral data of purple rice adulteration in the range of 0～7 THz was collected by Terahertz Time domain Spectroscopy (THz-TDS), and the absorption coefficient spectrum and refractive index spectrum of 0.5～2.5 THz band were selected for analysis and adopted. The chemometric method models and analyzes the spectral data. Savitzky-Golay convolution smoothing (SG smoothing), baseline correction (Baseline), normalization (Normalization), multiple scattering correction (MSC) and other methods are used for spectral preprocessing. Qualitative analysis of purple rice, purple rice mixed with rice and purple rice mixed with black rice was carried out by partial least squares decision analysis (PLS-DA). Qualitative analysis showed that there were significant differences in the plane distribution of the three samples by Principal Component Analysis (PCA); the PLS-DA model established by baseline corrected spectral data had the best effect, and the false positive rate was 0. Then using partial least squares (PLS) combined with SG smoothing, Baseline, Normalization, MSC and other pretreatment methods to establish a PLS quantitative model for the spectral data of the black rice mixed with dyed rice and purple rice. The results showed that the PLS model with baseline correction pretreatment method had the best effect. The correlation coefficient of the prediction set of purple rice-doped rice was 0.936, and the root means square error of prediction (RMSEP) was 0.095. The correlation coefficient of the prediction set of purple rice blended black rice was 0.914, and the root mean square error of the prediction set was 0.096. In order to compare and analyze the prediction accuracy of linear (PLS) and nonlinear (LS-SVM) quantitative model methods, the least squares support vector machine (least squares support vector) is established by using the same pretreatment method. Machine, LS-SVM) predictive model, using radial basis function (RBF) as the kernel function. The results showed that the LS-SVM model had the best effect after baseline correction. RMSEP of the predicted rice with purple rice was 0.092, and the correlation coefficient (Rp) of the prediction set was 0.979. RMSEP of the meter is 0.093, and the prediction set correlation coefficient (Rp) is 0.948. The comparison found that the LS-SVM prediction model for the content of purple rice adulteration is better and more accurate than the PLS model. Studies have shown that terahertz time-domain spectroscopy combined with chemometric methods can provide a fast and accurate analytical method for qualitative and quantitative analysis of purple rice adulteration.