There are four kinds of decoction pieces for Pinelliae Rhizoma, namely Pinelliae Rhizoma, Pinelliae Rhizoma praeparatum cum alumine, Pinelliae Rhizoma praeparatum cum zingibere et alumine, and Pinelliae Rhizoma praeparatum, recorded in the current Chinese Pharmacopoeia (2020 edition). Due to their similar appearance and weak odor characteristics, it's easy to confuse their manufacturing management, market circulation, and clinical application. Because of the requirement for instruments, reagents, and intricate detection steps, exploring and establishing an accurate, rapid, and non-destructive detection method for Pinelliae Rhizoma decoction pieces is necessary. This paper used hyperspectral imaging combined with machine learning to identify the four kinds of Pinelliae Rhizom adecoction pieces. The principal component analysis (PCA) was utilized to extract features from the hyperspectral data, and support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF) classification models were established based on the full-band data model. The accuracy of both training and test sets of four classification models was evaluated, along with an analysis of the principal component proportion of the four models under optimal performance. Additionally, the t-distributed stochastic neighbor embedding (t-SNE) visual dimensionality reduction analysiswas conducted on the hyperspectral data of the fourkinds of decoction pieces. The SVM, LR, MLP, and RF classification models based on PCA can achieve accurate identification for PinelliaeRhizoma decoction pieces. The accuracy of the test set is 80.76%, 96.45%, 96.59%, and 86.77%; in addition, the proportion of principal components is 60%, 80%, 70%, and 80%, respectively. The t-SNE analysis by dimensionality reduction showed that the components of Pinelliae Rhizoma praeparatum cum zingibere et alumine and Pinelliae Rhizoma praeparatum cum alumine were relatively close and partly changed compared with Pinelliae Rhizoma. However, the chemical composition of Pinelliae Rhizoma praeparatum changed greatly after processing, which was very different from the above three kinds of decoction pieces. These results also agree with the average spectral reflectance result. It's the first application of hyperspectral imaging combined with machine learning to develop a predictive model for different decoction pieces of Pinelliae Rhizoma. This approach -successfully identifies Pinelliae Rhizoma decoction pieces accurately, rapidly, and non-destructively, thereby providing a novel identification method and scientific foundation for these products' rational production, circulation, and clinical application.