In order to provide an efficient, rapid and non-destructive Inversion method for the nitrogen content of japonica rice leaves, based on the japonica rice plot experiment, using high spectrum technology and laboratory chemistry experiments to obtain the effective data for three growth periods of japonica rice in the tillering stage, joining stage and heading stage. A total of 280 sets of leaf high spectrum data and corresponding rice leaf nitrogen content data were obtained to analyze the spectral characteristics of japonica rice leaves with different nitrogen treatment levels. The random frog algorithm (Random_frog) is combined with the iteratively retaining informative variables algorithm (IRIV) to screen the feature bands, and any two spectral bands are randomly combined to construct the difference vegetation index DSI (Ri, Rj), ratio vegetation index. RSI (Ri, Rj) and normalized vegetation index NDSI (Ri, Rj), respectively, combine the superior feature band combination and vegetation index as model inputs. Therefore, BP neural network, support vector machine (SVR) and non-dominated elite strategy genetic algorithm optimization limit learning machine (NSGA2-ELM) japonica rice leaf nitrogen content Inversion model were constructed, and the model was verified and analyzed. The results showed that with the increasing level of nitrogen fertilizer treatment, the reflectivity of the japonica rice leaves in the near-infrared range gradually increased, while the reflectance decreased gradually in the visible range. A total of 8 characteristic bands were obtained by combining Random_frog and IRIV. Among them, there are 7 visible light bands, which are 414.2, 430.9, 439.6, 447.9, 682.7, 685.4 and 686.3 nm, respectively. Only one of the near-infrared bands is 999.1 nm. This method effectively eliminates interference information and greatly reduces the collinearity between the bands. At the same time, it can be analyzed from the three vegetation index (DSI (Ri, Rj), RSI (Ri, Rj), NDSI (Ri, Rj)) and the determination coefficient of the nitrogen content of the japonica rice leaves. DSI (R648.1, R738.1), RSI (R532.8, R677.3) and NDSI (R654.8, R532.9) have the best correlation with leaf nitrogen content, R2 are 0.811 4, 0.829 7 and 0.816 9, respectively. In the comparative analysis of the input parameters with different input parameters, the model Inversion with the feature band combination as the model input is slightly better than the vegetation index combination, R2 is greater than 0.7, and the RMSE is less than 0.57. In the comparative analysis between the Inversion models, the estimation effect of the NSGA2-ELMInversion model proposed in this paper is significantly better than the BP neural network model and the SVR model. The training set determination coefficient R2 is 0.817 2, and the root means square error RMSE is 0.355 5. The set R2 is 0.849 7 and the RMSE is 0.301 1. According to the results of this study, the Random_frog-IRIV screening characteristic band method combined with NSGA2-ELM modeling method has a significant advantage in rapidly detecting the nitrogen content of japonica rice leaves. The research results can provide a theoretical reference for field precision fertilization of japonica rice.