In order to establish a rapid method for discriminating Boletus edulis mushroom, Fourier transform infrared spectroscopy combined with multivariate statistical analysis were used to study B. edulis which were collected from different origins and different years. The original infrared spectra of all the 152 B. edulis samples collected from 2011 to 2014 and 26 different areas of Yunnan Province were optimized with orthogonal signal correction and wavelet compression (OSCW) method. The spectral data that before and after being preprocessed with OSCW were analyzed with partial least squares discriminant analysis (PLS-DA). The classification results of PLS-DA were compared. Then the 152 B. edulis samples were randomly divided into a training set (120) and a validation set (32) to establish the PLS classification prediction model. The results showed that, after OSCW processing, the classification result of PLS-DA was significantly better than the other one which was not processed by OSCW. Principal component score plot can accurately distinguish B. edulis samples collected from different years and different origins. It indicated that OSCW can effectively eliminate the noise of spectra and reduce the unrelated interference information about the dependent variables to improve the accuracy and calculation speed of spectral analysis. Before OSCW preprocessed, the R2 and RMSEE of PLS model of the training set were 0.790 1 and 21.246 5 respectively while R2 and RMSEP of the model of validation set were 0.922 5 and 14.429 2. After OSCW pretreatment, R2 and RMSEE of the training set were 0.852 3 and 17.238 1 while R2 and RMSEP of validation set were 0.845 4 and 20.87. It suggested that OSCW could improve the predictive effect of the training set, but the over-fitting of OSCW-PLS may reduce the predictive ability of validation set. Therefore, it was unsuitable to establish a model with OSCW combined with PLS. In a conclusion, OSCW combined with PLS-DA can eliminate a large amount of spectrum interference information. This method could accurately distinguish B. edulis samples collected from different years and different origins. It could provide a reliable basis for the discrimination and classification of wild edible fungi.