Alfalfa (Medicago sativa L.) is the most commonly grown forage crop due to its better quality characteristics and high adaptability in China. However, there was 20%~80% hard seeds in alfalfa which could not be identified easily from non hard seeds which would cause the loss of seed utilization value and plant production. This experiment was designed for 121 samples of alfalfa. Seeds were collected according to different regions, harvested year and varieties. 31 samples were artificial matched as hard rates ranging from 20% to 80% to establish a model for hard seed rate by near infrared spectroscopy (NIRS) with Partial Least Square (PLS). The objective of this study was to establish a model and to estimate the efficiency of NIRS for determining hard rate of alfalfa seeds. The results showed that the correlation coefficient (R2cal) of calibration model was 0.981 6, root mean square error of cross validation (RMSECV) was 5.32, and the ratio of prediction to deviation (RPD) was 3.58. The forecast model in this experiment presented the satisfied precision. The proposed method using NIRS technology is feasible for identification and classification of hard seed in alfalfa. A new method, as nondestructive testing of hard seed rate, was provided to theoretical basis for fast nondestructive detection of hard seed rates in alfalfa.