501 milk samples of healthy Chinese Holstein cows were collected from major milk-producing regions in Northwest，North，and Central China to establish a method that can rapidly，in batch，and efficiently detect the content of β-lactoglobulin in milk from Chinese Holstein cows，high-performance liquid chromatography （HPLC） was used to determine the content of β-lactoglobulin in milk samples，and the mid-infrared spectroscopy （MIRS） data of milk samples were synchronously measured and collected 12 methods of spectra pretreatment were randomly combined twice in a row，and the characteristic bands were manually selected with MIRS as the predictor variable and the content of β-lactoglobulin as the dependent variable.Partial least squares regression （PLSR） was used as a traditional machine learning algorithm to establish an optimal model for the prediction of the content of β-lactoglobulin in milk.The results showed that the R_C² and R_P² of the cross validation set and test set in the established model was 0.812 9 and 0.768 8，with the root mean square errors，RMSE_C and RMSE_P of 0.476 2 g/L and 0.524 9 g/L，the RPD of 2.076 6，meeting the requirements for measuring the production performance of livestock and poultry.It is indicated that MIRS can be used to establish a model for predicting the content of β-lactoglobulin in milk from Chinese Holstein cows.