Crude protein (CP) is the key parameter for evaluating nutritive value and quality of pasture. It has a great significance for evaluating crude protein content of pasture quickly and accurately in animal husbandry. For confirming the hyperspectral characteristic bands and optimal detection model of crude protein content in pasture, we randomly selected thirty-five sample plots each month from May to September, 2017 in Dorbet, Heilongjiang Province, one hundred and seventy-five samples for all. A 1 m×1 m quadrangle was placed at the sample point during sampling, and all the aboveground pastures in the quadrangle were collected, weighed and stored in cold storage. After carrying the samples the laboratory, we collected the hyperspectral information immediately and determined the chemical values of crude protein by Kjeldahl determination, establishing the hyperspectral dataset of crude protein content. We used five pre-processing methods including SG, MSC, SNV, 1-Der, DOSC to process the hyperspectral data and then, built the PLSR models for confirming the optimal pre-processing method. Based on the optimal pre-processing result, the characteristic bands of crude protein were selected by successive projections algorithm and random frog algorithm, then the PLSR models were built for confirming the optimal selection method of characteristic variables and the optimal hyperspectral detection model. The results showed that the hyperspectral detection model based on SNV was the best in the five pre-processing methods. Thirty bands were selected by SPA and distributed in 530 to 700 nm and 940 to 1 000 nm. Six bands were selected by RF, and respectively were 826.544, 827.285, 828.766, 971.012, 972.494 and 973.235 nm. Therefore, the optimal hyperspectral detection model was SNV-RF-PLSR in this research, and the accuracy of model was good. The results of this research provided an optimal model and theoretical basis for hyperspectral detection of crude protein in pastures and in addition, developed new technique solutions for guiding the production of grassland industry.