The whole spectrum usually contains a lot of redundant information in the near-infrared spectroscopy model, the presence of redundant information will increase the model resolution time and increase the difficulty of parsing model, Therefore, how to select the characteristic wavelength quickly and effectly is very crucial. In this paper, we combined the algorithm based on SPA (successive projections algorithm ) with IPLS (interval partial least squares ) to selec the characteristic wavelength in the fermentation of wheat straw microbial biomass, A total of 85 samples prepared by measuring microbial biomass using glucosamine method, 68 samples are chosen as calibration set and 17 simples are chosen as verification set. First, the whole spectral region 520 points are segmented modeling according to the interval wavelength point size 10, 20, 30, 40 and 4 450~4 925 cm-1, 9 194~9 993 cm-1 two-band range are selected as the characteristic wavelength band, then pick out the new feature wavelength points by Successive Projections Algorithm band and Genetic Algorithm (GA), comprehensive analysis and comparison the result of model. The experimental results show that the using of IPLS-SPA algorithm to select the combination band 4 450~4 925 cm-1 & 9 194~9 993 cm-1 has the best modeling effect, compared with the modeling of whole spectrum, the wavelength points decrease from 520 to 10, the correction coefficient of determination R2 rised from 0. 884 9 to 0. 945 28, root mean square error (RMSE) dropped from 11. 104 9 to 8. 203 3, although the genetic algorithm model achieved the better accuracy, but the results are instable and have a strong randomness , while IPLS combined SPA method can select characteristic wavelength information stability and accurately, which can improve the model calculation speed and reduce the fitting difficulty of the model, it can be used as a new reference method for band selection. The results show that using near infrared spectroscopy method for straw biomass rapid detection is feasible.