To achieve rapid, non-destructive, and accurate traceability of Phellinus linteus samples, this study compared the application of dual-band hyperspectral imaging in the visible-near-infrared (400-1000 nm) and short-wave infrared (900-1700 nm) regions combined with deep learning algorithms to construct a model for rapid identification of the origin and cultivation mode of P. linteus. Six preprocessing techniques were compared, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay smoothing, and detrending, along with three feature selection algorithms: successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variables elimination (UVE), as well as three deep learning algorithms: convolutional neural network (CNN), back propagation neural network (BPNN), and radial basis function neural network (RBFNN). The optimal algorithm combination was determined through these comparisons. The results showed that the short-wave infrared band provided significantly higher identification accuracy than the visible-near infrared band. Among the three deep learning algorithms, the CNN model demonstrated the best classification capacity. Specifically, for P. linteus origin traceability, the SNV-UVE-CNN model in the 900–1700 nm range exhibited the best performance, with a classification accuracy of 99.36% on the test set. For cultivation mode recognition, the MSC-CNN model in the 900–1700 nm range performed optimally, with a classification accuracy of 97.44% on the test set. Additionally, t-distributed stochastic neighbor embedding (t-SNE) was employed to visualize the deep features extracted by the model. The findings demonstrated the advantages of the 900–1700 nm spectral range and confirmed the suitability of the CNN model for the identification of the origin and cultivation mode of P. linteus. This study provides a pivotal theoretical foundation and essential data support for the development of intelligent rapid detection systems and the advancement of portable detection technologies.