Agaricus bisporus is fragile and nutritious, which helps lower blood pressure, lowering blood lipids, reducing inflammation and protecting the liver. The freshness is one of the most important indicators to reflect the internal and external quality of Agaricus bisporus. At present, the freshness identification of Agaricus bisporus is mostly based on appearance quality (browning), and there is a lack of an accurate quantitative evaluation method. Therefore, in this research, a quantitative index for freshness detection was proposed based on storage days, which was used to analyze the freshness of Ag aricus bisporus with VIS-NIR spectroscopy technology. According to the different storage days, the samples of Agaricus bisporus were divided into 1 to 5 groups, each with 40 samples, and the near-infrared spectral data of each group was collected in turn using a fiber optic spectrometer. For the collected raw spectral data, firstly, the SG and MSC transform methods were selected to correct and eliminate the effects of spectral noise, baseline shift and light scattering. Moreover, the spectral band sranging from 399.81 to 999.81 nm were selected as the data processing range simultaneously.Then the method of principal components analysis (PCA) and successive projections algorithm (SPA) were respectively used to reducethe spectral dimensionalities and select the characteristic wavelengths. And the Extreme Learning Machine (ELM) classifier was established based on the spectral features. Since the initial parameters have a greater impact on the classification accuracy of the ELM model, the Particle Swarm Optimization (PSO) and Seagull Optimization Algorithm (SOA) was used to optimize the initial values of weight and threshold for ELM classifier to establish PSO-ELM and SOA-ELM classifiers. Finally, the full spectrum, the extracted principal components and the selected characteristic wavelengths {556.87, 445.51, 481.15, 885.10, 802.25, 720.90, 861.34, 909.79, 924.44, 873.17} nm were input into the classification model to establish the freshness detection model of Pleurotus ostreatus with different inputs and different classification models. The final test results show that when the ELM is the classification model, the prediction accuracy with full spectrum, principal component and characteristic wavelength as input is 75%, 95% and 88% respectively; the training set accuracy of PSO-ELM and SOA-ELM classification model with SPA preferred characteristic wavelength as input is 96.25%, 93.25%, and the accuracy of prediction set is 92.5%, 94%. It can be seen that the method of SPA was effective to reduce the redundant information of VIS-NIR spectra and accelerate the modeling. At the same time, the SOAwas better to optimize the initial parameters of the ELM classifier and significantly improve the classification accuracy, and the classification accuracy is 6.8% higher than that of the ELM model. Therefore, the freshness of Agaricus bisporus can be identified quickly and accurately by using spectral features. The research results provide a theoretical basis for the development of portable equipment for rapid non-destructive testing of the freshness of Agaricus bisporus.