Heilongjiang Province is the largest japonica rice producing area and commodity grain base in China. In the process of rice planting, selecting suitable rice varieties is the key to achieving high yield. In agricultural production, the selection of rice varieties is influenced by factors in many aspects. Generally speaking, different rice varieties planted in the same temperate zone have little difference in appearance, or even no difference. It is difficult to make an accurate distinction by visual observation. In order to accurately distinguish different varieties of japonica rice seeds that are difficult to distinguish by naked eyes, a rapid non-destructive discrimination method for japonica rice based on near-infrared spectroscopy (NIRS) was proposed. 3 varieties of japonica rice seeds (seeds 5th, seeds 6th and Sui japonica 4th) planted in Heilongjiang reclamation area were selected as the research object. For each variety, 40 samples were selected, 30 of which were used as modeling set and 10 as prediction set. The NIRS data of all 120 samples were obtained by scanning. The noise at both ends of the original spectral data (11 520～4 000 cm-1) were clipped, the spectral data in the range of 8 250～5 779 cm-1 with strong absorbance were selected as the research band. Firstly, a reference model was established, that is, BP model 1 was established directly from raw spectral data, and BP model 2 was established from the spectral data preprocessed by first derivative (FD) and Savitzky-Golay (SG). The classification accuracy of model 1 was 93.3% with RMSEP=0.232 8, and the iteration time was t=3 882.9 s. The classification accuracy of model 2 was 100% with RMSEP=0.070 6, and the iteration time was t=954.5 s. Comparing the evaluation parameter RMSEP of the two models, it was found that FD+SG preprocessing can improve the prediction ability of the model. However, because the two models do not reduce the dimension, the amount of data is too large, the input nodes of the model are too many and the iteration time is too long, which is not conducive to the practical application. Therefore, the wavelet transform with multi-resolution characteristic was used to reduce the dimension of the data. The residual sum of squares of the prediction set (Press value) were used as the evaluation index. Sym2(symlet2) wavelet with decomposition scale 5 was selected to compress and reduce the dimension of the spectral data from 601 dimension to 21 dimension. The results of wavelet transform were used as the input of BP model 3, which was compared with model 1. The classification accuracy of the model 3 was 93.3% with RMSEP=0.225 0, and the iteration time was shortened to 198.9 s. The comparison results showed that dimensionality reduction based on wavelet transformation can reduce the input of the neural network, thus simplifying the structure of the neural network and improving the iterative speed, but the effect of improving the prediction ability of the model is not obvious. The comparison results of the three models showed that FD+SG preprocessing can improve the prediction ability of the model, and the wavelet transform can improve the iteration speed of the model. Based on above analysis results, a neural network discrimination model 4 with 21 inputs, 15 hidden layers and 3 outputs of FD+SG+wavelet transform was established. Moreover, its recognition rate of classification was 100% with RMSEP=0.029 3 and the iteration time was t=98.8 s, which could identify three different japonica rice varieties quickly, accurately and non-destructively. Therefore, the method of wavelet reduction and back propagation artificial neural network (BP) discrimination model based on near infrared spectroscopy can be used for rapid and nondestructive discrimination of japonica rice seeds, providing a reference method for other crop seeds recognition.