Abstract: The honey is worth of nectar plant. A rapid non-destructive method of pattern classification for nectar plant was developed based on visible-near infrared spectroscopy in this study. The nectar plants came from four categories which were Tilia, Astragalus, Leguminosae and Wild hrysanthemum, respectively. A total of 232 samples from four different nectar plants were studied. The calibration set was consisted of 212 samples and the predict set consisted of 20 samples. The classifier was constructed by calibration set which was selected randomly while prediction set was used for evaluating the study ability of classifier. The preprocessing methods were carried on the spectrum data, such as base line correction, normalization and smoothing. The preprocessing methods can enhance signal to noise ratio and remove the random error. The two classifier models were developed using pricipal component analysis combined with Bayesian line discriminant analysis based on one-two-many rule method and backpropagation atificial nerve net method. The accuracy of pricipal component analysis combined with Bayesian line discriminant analysis model was 91.95% and that of the BP- atificial nerve net model was 100%. The results indicated that the nectar plant would be quickly detected by Vis-NIR spectroscopy technique, and it is very feasible.