Traditional rice seed germination rate detection methods have low efficiency, poor accuracy and high specialization. The paper proposed a novel method by using fluorescent spectrometry combined with Deep Belief Network (DBN) to establish forecasting model for rice seed germination rate. Firstly, two varieties of seeds, Lianjing 7 and Wuyunjing, with 0～7 artificial aged days separately were soaked into purified water for 5～30 minutes with every 5 minutes’ interval. Then the fluorescence spectrums of the soak solutions were detected using fluorescence spectrometer. In addition, the spectrum data were centralized and then denoised with Ensemble Empirical Mode Decomposition (EEMD). The characteristic fluorescence wavelength of 441.5nm was extracted using Principal Component Anamysis (PCA). Finally, the rice seed germination predicting models were establishee with Partial Least Squares Regression (PLSR), Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN) and Deep Belief Network (DBN), respectively. The results showed that the accuracy of DBN model was the highest in the case of less data and weak signal, and Rp=0.979 2, RMSEP=0.101. At the same time,we got the best soaking time is 22.1 min by analyzing the changing trend of mixed rice seed fluorescent data Rp, actually, it took about 5 min to get the accuracy more than 0.95 (Rp). The research results demonstrated the feasibility and high accuracy for predicting rice seed germination rate non-invasively by combining the fluorescent spectrometry and EEMD-DBN model, moreover, it adapts to the detection of rice seeds with different colors and contaminated levels.