With respect to the shortcomings of traditional rice seed germination rate detection such as more time consumption, and the problems of near infrared spectroscopy that it is easily influenced by natural color and water content of rice seeds, a method based on continuous polarization spectroscopy and inlaid grey neural network to achieve rapid and nondestructive prediction of rice seed germination rate is proposed. The obtained continuous polarization spectra are de-noised by the empirical mode decomposition (EMD) and wavelet packet transform, and EMD is selected according to the de-noising effect. The characteristics of de-noised continuous polarization spectra are extracted by the principal component analysis (PCA) and four modeling methods are used to build rice seed germination rate prediction models including partial least squares regression (PLSR), back propagation neural network (BPNN), radial basis function neural network (RBFNN) and inlaid grey neural network (IGNN). The modeling results show that the IGNN model at 10 min testing time is the most accurate, with the correlation coefficient of prediction set as 0.985 and mean square error of prediction set as 0.771. The research results show that the method based on continuous polarization spectroscopy and inlaid grey neural network can achieve rapid and nondestructive prediction of rice seed germination rate and has high prediction accuracy.