At present, near-infrared spectroscopy (NIRS) technology, can realize the rapid and non-destructive detection of seed vigor, but the vigor grade is generally less than 3, and the accuracy is not high.The contradiction between the increase of vigor level and model precision urgently needs to be solved in the near-infrared spectrum detection of seed vigor. Five kinds of seed samples were obtained by the artificial aging method, and the corresponding spectral data were collected to establish the BP prediction model. In order to improve the accuracy and robustness of the model, an algorithm of coupled Mean Impact Value-Successive Projection Algorithm (MIV_opt-SPA_sa) is presented. Aiming at the problem of determining the number of feature variables extracted by the Successive Projection Algorithm(SPA), the algorithm sets the number range of feature wavelengths and selects the best in this range to realize adaptive SPA(SPA_sa). Aiming at the problem that SPA algorithm takes a too long time, MIV algorithm is used to reduce the dimension of SPA algorithm. Although the MIV method can sort the wavelength influence values, it lacks the threshold value for selecting wavelength influence. Therefore, the relative distance ratio is introduced to optimize the MIV algorithm to effectively segment the characteristic wavelength range. The full spectrum with 1 845 wavelengths is extracted by the MIV_opt-SPA_sa algorithm, and 37 characteristic wavelengths are extracted, which are mainly distributed near the 7 main absorption peaks of near-infrared spectrum of maize seeds. The results show that the algorithm can effectively extract the characteristic wavelength, which is consistent with the NIR absorption characteristics of maize seed biochemical substances. In order to verify the effect of the algorithm on the performance of the model, the full spectrum BP model, SPA_sa-BP model, MIV-BP model, MIV_opt-SPA_sa-BP model and competitive adaptive reweighting CARS-BP model were established to classify the five grades of maize seed vigor. The average prediction accuracy of the MIV_opt-SPA_sa-BP model is 99.1%, which is higher than other models; the average prediction time is 14.382 s, which is lower than that of the MIV-BP model (24.523),CAR-BP (97.226) and SPA_sa-BP model (101.224 s), but higher than that of full-spectrum model (0.253 1); The best performance cross-entropy is 0.007 892, which is far lower than other 4 models. The experimental results show that the MIV_opt-SPA_sa algorithm can effectively improve the accuracy of the near-infrared detection model of maize seed vigor, realize multi-level, accurate and nondestructive detection of seed vigor, and provide a reference for optimizing the optimisation seed vigor detection model.