Near-infrared spectroscopy (NIRS) is a non-destructive detection method for qualitative or quantitative analysis by using spectral feature data. The integrity and representativeness of feature data determine the performance of the analytical model. However, existing analytical methods can only extract the feature data from the spectral subinterval. Then the developed models using these feature extracting methods have poor stability. In order to extract the feature from the high-dimensional NIR spectral data and improve the accuracy and stability of NIR spectral model, a spectral screening method using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is proposed in this paper. Furthermore, the Tricholoma Matsutake, one of the high-value foreign trade products in China is taken as example to validate the developed classified model using LASSO algorithm. The effectiveness of the feature screening algorithm for the high-dimensional spectral data is discussed, and predictive accuracy and stability of the Tricholoma Matsutake distinguished and edible fungus classified model using LASSO and PCA are also analyzed. It is well known that the fresh Tricholoma Matsutake has the unique shape and it is easy to distinguish its counterfeit. However, it is difficult to distinguish the dry Tricholoma Matsutake from other mushrooms because all of dry mushrooms have the similar flake shape. As a result, dry Tricholoma Matsutake adulteration incidents have occurred frequently. 166 dry samples of Yunnan Tricholoma Matsutake, Pleurotuseryngii, Jujube hilt nipple mushroom and Agaricusblazei were selected in this experiment, and 166×512-dimensional raw spectral data were obtained by NIRQuest 512 NIR spectrometer with a spectral range of 900～1 700 nm. The standard normal transformation (SNV) was taken to pre-process the spectral data after the anomalous data eliminating. The LASSO was used to extract feature variables from the high-dimensional NIR spectral data based on the spectral pretreatment. Then the typical linear (k-Nearest Neighbor, KNN) and the nonlinear modeling (Back-Propagation neural network, BP) algorithms combined with the Kennard-Stone method were used to construct the Tricholoma Matsutake distinguished and edible fungus classified model. The effectiveness of models using LASSO and PCA were also analyzed. Furthermore, the predictive accuracy and the stability of the developed KNN model and BP model were analyzed by using the Monte Carlo method. The experimental results demonstrated that the prediction accuracy and stability of model using LASSO were better than those of the model using PCA. The prediction accuracy of the distinguished and edible fungus classified models using the original spectral data were 69.57% (BP), 60.87%(KNN) and 67.39% (BP), 65.22% (KNN) respectively. And the prediction accuracy of the distinguished and edible fungus classified models using LASSO algorithm were up to 100% (BP), 67.39% (KNN) and 89.13% (BP), 80.43% (KNN) respectively. The two models were performed by 10 times Monte Carlo method and the average results were 99.93% and 97.22%, respectively. Compared with the conventional feature selection methods (such as PCA), the LASSO algorithm can extract the feature from the high-dimensional NIR spectral data. And the accuracy and stability of the models using NIR spectral data can be improved. Furthermore, the developed algorithm is alternative to be a new feature extraction method for NIR spectral data analysis.