Pleurotus ostreatus is one of the wide varieties of edible fungus, ranking third for its yield in China. Except for its delicious taste, appreciated by consumers, it is known to be rich in high-quality protein and polysaccharides with various biological activities. However, there are different kinds of P. ostreatus following their quality, and the existing nutrient composition analysis methods are time-consuming and high in composition. It is difficult to meet the requirements of the detection of their nutrient composition, as well as for other edible fungi. Fourier Translation Infrared Spectroscopy (FTIR) technology, characterized by high-speed detection, convenient technique, simultaneous analysis of multiple compounds, and safe and environmental protection, was thus used combined with stoichiometry to develop mathematical models, to assess those nutrient compounds. Therefore,the infrared spectra of 85 samples from P. ostreatusas fruiting bodies collected nationwide were determined. 5 kinds of spectral data pretreatment methods, multiple scatter correction (MSC), standard normal transformation (SNV), orthogonal signal correction (OSC), smooth plus first derivative (F-GD), and smooth plus second derivative (S-GD) were used. Following the model of the validation set regression coefficients, OSC combined with S-GD, and OSC combined with F-GD were the best pretreatment methods for the fruiting body protein and polysaccharide models. Under the optimal spectral pretreatment, 7 458 spectral bands were extracted by the LASSO algorithm, and 93 characteristic wavenumbers of protein and 92 for polysaccharides were obtained, with a compression rate of 98%. PLS model was established by fitting the characteristic wavenumbers with the protein and polysaccharide contents of P. ostreatus fruiting bodies detected by chemical method. The results showed that, for the protein model, the R2 regression coefficient of the calibration set was 0.999 8, RMSECV was 0.047 7, the R2 regression coefficientof the validation set was 0.987 2, RMSEP was 0.506 8, and RPD was 8.840 6 greater than 3, while for polysaccharides model, The R2 regression coefficient of calibration set was 0.999 9, RMSECV was 0.020 1, the R2 regression coefficient of validation set was 0.980 3, RMSEP was 0.292 9, and RPD was 7.119 8 greater than 3. The models thus had good predictive ability and robustness. This research provides a practical reference to determine a high-speed detection method for the nutrient content ofedible fungi by FTIR, a foundation to establish a nutritional quality evaluation for P.ostreatus and the promotion of their high-quality development, even for other edible fungi.