Reed canary grass (Phalaris arundinacea L.) is a perennial cool-season gramineae grass with a high yield. Crude protein (CP) is a key indicator in the evaluation of forage quality, but the use of chemical analytical methods to determine the CP content is disadvantageous. Therefore, a fast, efficient, accurate, and safe determination method is required in the development of modern grassland agriculture, animal husbandry and grassland ecological restoration. The purpose of this study is to use near-infrared spectroscopy (NIRS) techniques to develop a quantitative model for the analysis of CP in reed canary grass and provide an effective method for a rapid determination. We collected 454 samples of reed canary grass from various resources, including different cultivars (or strains), different growth stages, different cultivation conditions, different drying methods, different growth years, different parts and different harvest times. The original spectra of all of the samples were obtained using a near-infrared spectrometer (NIRFlex N-500) and Operator software of the Swiss Buchi company. A total of 210 samples were selected for the development and evaluation of models after deleting samples with similar spectra by a K-S algorithm, and were assayed using the Kjeldahl nitrogen method to obtain the chemical values of CP; we then assigned them to spectra in a Management console software. The samples were randomly divided into a calibration set and a validation set according to the proportion of 6∶3, using the NIRcal 5.4 software; the outliers were then eliminated. We established 8 quantitative analysis models for the CP content of reed canary grass by applying different spectral data pretreatments, primary/secondary principal components, spectral regions, and regression algorithms. We revealed that all of the 8 models can be used in the determination of CP by performing an external validation. The best model was chosen by comparing statistic parameters. The results showed that the best calibration model was developed by the spectral data pretreatment of sa3+ncl+db1 (smoothing average 3 points+ normalization by closure+first derivative BCAP), choosing the primary/secondary principal component of 8/1-4 and spectral region of 4 000~10 000 cm-1 in combination with the partial least square (PLS) regression algorithm. Its calibration coefficient of determination (R2cal) and external validation coefficient of determination (R2val) were 0.982 1 and 0.980 2, respectively; both were larger than 0.98, suggesting an excellent predictive ability. The standard errors of calibration (SEC) and prediction (SEP) were 0.780 2 and 0.783 2, respectively; both were very small and similar, which demonstrated the high analytical accuracy and robust fitting. The bias value of -0.000 5, close to 0, demonstrated the model’s stability and robustness, i. e., its insensitivity to the external factors. The correlation coefficient of validation (r) of 0.99 indicated a very high correlation between the predicted and chemical values. The residual predictive deviation (RPD) was 7.37 (above 4.0), further confirming that the CP model can be used for a high-quality quantitative analysis. Therefore, in this study, a quantitative model for a CP analysis of Phalaris arundinacea L. was developed using NIRS for the first time in China with a large data collection from different sources and high accuracy, which guaranteed the reliability and practicability. The model provides an effective method to quantify CP of reed canary grass for a rapid screening of germplasm in breeding programs, optimization of the allocation of livestock diets, and classification of forage products in the supply chain.