Plastrum Testudinis is a popular traditional Chinese medicine (TCM) with abundant medicinal and edible value, and it is widely applied to clinical medical treatment and medicinal slice preparation. Studies show that the contents of trace elements in Plastrum Testudinis are directly proportional to its growth years. However, due to inexperience and nonstandard breeding, adulterated Plastrum Testudinis medicines are on the market. Because of the limitation of empirical and chemical-based methods, a heterogeneous ensemble learning (HEL) method based on a hyperspectral imaging technique is proposed to identify the growth years of Plastrum Testudinis. First, the Plastrum Testudinis samples with different growth years are taken as research objects. The original hyperspectral images of visible near-infrared ray (VNIR) and short-wave infrared ray (SWIR) lenses are captured on the hyperspectral imaging system. Then, the heterogenous ensemble learning (HEL) model is constructed based on support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN). Results show the fused hyperspectral images of VNIR and SWIR include more abundant spectral information. The HEL model can achieve satisfactory prediction ability by identifying the different growth years of Plastrum Testudinis samples. In addition, considering the detection efficiency, an unsupervised band selection is employed to reduce the complexity, eliminate the redundant bands in hyperspectral images, and improve the classification performance further. When the number of selected spectral bands is 32, the classification accuracy reaches 96.35%. Experimental results demonstrate that the HEL model based on hyperspectral imaging can accurately and rapidly identify the different growth years of Plastrum Testudinis samples and provide a novel technique reference for the attributes identification of TCM.