Protein powder is an essential nutritional supplement for bodybuilders, and the market demand is increasing. Some unscrupulous businessmen are adding cheap powder to protein powder for sale to profit. The traditional protein powder adulteration detection method is time-consuming, laborious, complicated and expensive. Hyperspectral technology has the advantages of easy operation and rapid detection without damaging the experimental sample. Therefore, this paper proposes the use of hyperspectral technology to achieve protein powder adulteration detection. In the experiments, three types of adulterants (corn flour, rice flour and wheat flour) with 5%~60% mass percentages and 5% concentration interval were added to the protein powder, and the spectral information of all samples was collected. In the qualitative discrimination of the three types of adulterants (corn flour, rice flour and wheat flour) in the protein powder, the spectral data were firstly processed using the pre-processing methods of convolutional smoothing (SG), normalization (Normalize), multiple scattering correction (MSC), baseline correction (Baseline) and standard normal transformation (SNV), and then the spectral data were established based on principal component regression ( PCR), backpropagation neural network (BPNN), and random forest (RF) models, among which the RF model built under the MSC preprocessing method based on full-band spectra is the best, and its overall accuracy reaches 100%. Its corresponding R_P and RMSEP are 0.997 9 and 0.018 9, respectively. In the quantitative analysis of different adulterant concentrations in protein powder, the spectra of the three types of adulterated samples were pretreated with SG, Normalize, MSC, Baseline and SNV, respectively, and LSSVM models were established. The performance between the models under different pretreatment methods was compared. The best LSSVM prediction models were used for corn flour, rice flour and wheat flour adulterated in protein powder preprocessing methods were None, Baseline and Normalize, and then, the continuous projection algorithm (SPA) and competitive adaptive reweighting algorithm (CARS) were used to screen them and build LSSVM models. The RP corresponding to the SPA-LSSVM models for the three types of adulterated samples were 0.989 0, 0.986 0 and 0.997 9, and the R_P of the CARS- LSSVM model corresponds to R_P of 0.991 0, 0.994 6 and 0.999 1, so the CARS-LSSVM model for the three types of adulterated samples has a better prediction. Research shows that hyperspectral technology can achieve qualitative and quantitative detection of protein powder adulteration and simple operation, rapid and non-destructive detection.