Pleurotus ostreatus is popular with consumers because of its delicious taste and rich nutrition. Pleurotus ostreatus is widely cultivated in China, and its producing areas are scattered. The differences in climate conditions, cultivation matrix and cultivation mode of each producing area make the Pleurotus ostreatus produced in different producing areas different in taste and nutritional value. In order to standardize the market management of Pleurotus ostreatus products and create regional characteristics of Pleurotus ostreatus brands, with the help of the characteristics of non-pollution, high efficiency and low cost of mid-infrared spectroscopy, this paper broke through the limitations of chemical analysis and biological identification methods at present, and put forward a method of identifying Pleurotus ostreatus from different producing areas by mid-infrared spectroscopy combined with machine learning. The infrared spectrum data of fruiting bodies of Pleurotus ostreatus from 10 different producing areas were collected, and 60 samples were collected from each area. The analysis of the spectral data showed that the correlation of the infrared spectra showed significant differences in the band 530～1 660 cm-1. At the same time, based on the K-S method, the samples were divided according to the ratio of the training set to test set of 7∶3, 420 training sets and 180 test sets were obtained. Multiplicative scatters correction (MSC), standard normal variable transformation (SNV), Smoothing(SG), first derivative (FD), second derivative (SD) and other preprocessing methods were used to optimize the spectrum and remove the noise. In addition, it combined with a support vector machine (SVM) for preliminary modeling comparison. It was concluded that the difference in spectral data after MSC pretreatment was the largest, and the recognition performance of the prediction set was the best at 84.44%. The MSC spectral data is normalized in 0-1, and principal component analysis (PCA) was used to reduce the dimension. The first seven principal components, which satisfy the cumulative contribution rate of principal components in the training set ≥85% and the variance percentage of principal components ≥1%, were selected as input variables for modeling identification comparison with support vector machine (SVM), random forest (RF) and extreme learning machine (ELM). The experimental results showed that the SVM model had the best recognition effect in identifying Pleurotus ostreatus models from different producing areas, and the recognition rate of the training set and test set was 100%. The recognition rate of the RF model training set was 100%, and the recognition rate of the test set was slightly lower, 98.89%. Compared with other models, the recognition rate of the ELM model was poor, the recognition rate of the training set was 99.28%, and that of the test set was 98.33%. The recognition rates of the three models were all higher than 98%, indicating that the identification of Pleurotus ostreatus from different producing areas can be realized, quickly and at low cost using infrared spectroscopy combined with machine learning. This provided a method basis for the producing areas identification of Pleurotus ostreatus products and a reference for the identification of other kinds of edible fungi products’ producing areas.