The problem of heavy metals exceeding the standard in Chinese medicinal materials is becoming increasingly serious, which will hinder the high-quality development of the Chinese medicine industry in the future. Therefore, research on efficient, accurate and convenient methods for the identification of excessive heavy metals is of great value for understanding the safety of traditional Chinese medicine. X-ray fluorescence spectrometry (XRF) instruments have the advantages of non-destructive testing, fast and accurate, and convenient sample preparation, and are widely used in elemental analysis. Due to the low threshold of heavy metals in traditional Chinese medicinal materials (for example, the 2020 edition of the Chinese Pharmacopoeia stipulates that the lead exceeds the standard at 5 mg·kg-1), there are many types of traditional Chinese medicines, complex matrices, and lack of national standard samples. Conventional classification algorithms are difficult to identify excessive problems accurately. This paper combines transfer learning with a multi-class support vector machine (TrAdaBoost SVM) method. The spectral feature information of national soil standard samples similar to honeysuckle is used for data enhancement, and the standard soil sample and a small amount of traditional Chinese medicine samples are mixed with establish Transfer learning and support vector machine classification models. Through the experimental verification, the classification optimization method combining transfer learning and TrAdaBoost-SVM, compared with the traditional SVM and AdaBoost classification algorithm, the accuracy rate of identifying the heavy metal element lead (Pb) exceeding the standard has been significantly improved. Through the prediction verification of the test dataset, the prediction accuracy of the TrAdaBoost-SVM model is 96.7%, which is higher than that of the traditional SVM and AdaBoost classification models. The method of combining transfer learning and TrAdaBoost-SVM proposed in this paper can establish a classification model under the condition of small samples and can accurately predict the excess of heavy metals in traditional Chinese medicine, which has certain theoretical significance and application value.