Near-infrared diffuse reflectance spectroscopy was used to determine the hardness of five different fruits (including green head, rough skin, dislocated, scorpion, and apex) of Xinjiang pear fruit Korla pear. Due to the large amount of data in the near-infrared spectrum, the original spectral noise is obvious, and the scattering of fruits is serious, the key wavelength variables are difficult to extract during spectral modeling. Based on this, in order to effectively eliminate the influence of solid surface scattering and optical path variation on the NIR diffuse reflectance spectrum, it is proposed to use standard normal variable transformation (SNV) and multiple scattering correction (MSC). The original spectrum of Korla pear was pretreated. In order to find the best characteristic wavelength screening method suitable for the detection of Korla pear hardness by near-infrared spectroscopy, the comparison and research on the characteristic wavelength variable selection methods of Pear near infrared spectrum were carried out. The effects of two characteristic wavelength screening methods on the modeling accuracy of Korla pear hardness partial least squares (PLS) were compared. Simultaneously using the reverse partial least squares (BiPLS) and genetic algorithm combined with reverse partial least squares (BiPLS-GA) to screen the characteristic wavelength variable of the pear hardness in the whole spectral range, the corrected root mean square error (RESMC), The prediction root mean square error (RESMP) and the decision coefficient (R2) were used as the evaluation criteria of the model, and the optimal band selection method and the optimal prediction model were finally determined. The PLS model based on the selected characteristic wavelength variable (BiPLS-GA) was compared with the PLS model established by the full spectral variable. It was found that the BiPLS-GA model obtains better information than the full-variable PLS model by using only 66% of the information in the original variable. The prediction results of Korla pear hardness, where R2, RMSEC and RMSEP are 091, 103 and 101, respectively. Furthermore, compared with the PLS model established by the feature variables obtained by the reverse partial least squares algorithm (BiPLS), BiPLS-GA can not only remove the non-information variables in the original spectral data, but also compress and remove the collinear variables, reducing the number of modeling variables from 301 to 20. The model is greatly simplified while the prediction accuracy and stability of the model are effectively improved. Therefore, the method can be effectively used for the selection of near-infrared spectral data variables. It is proved that the near-infrared spectroscopy analysis technology combined with the BiPLS-GA model can efficiently select the modeling variables, remove the near-infrared spectral information unrelated to the hardness of Korla pear, and significantly improve the prediction accuracy of the Korla pear hardness quantitative model. This not only provides a certain technical support for the rapid, precise and non-destructive optimization of the characteristic pear fruit Korla pear in Xinjiang, but also provides a reference for the research of predicting the internal quality of fruit based on near-infrared spectroscopy.