Puerariae Thomsonii Radix is a medicinal and edible plant with an extremely high medicinal and edible value containing puerarin, starch, cellulose, vitamins, etc. Extensive research has shown that the content of chemical components in Puerariae Thomsonii Radix is closely related to the growth period. However, much of the research up to now has been descriptive. The main disadvantage of traditional techniques is that the operation cycle is long, and the destructiveness is large, which cannot be tested on a large scale. The development of hyperspectral imaging (HIS) has provided new insights for the rapid non-destructive identification of Puerariae Thomsonii’ s age.In order to avoid the quality problems caused by the insufficient growth years of Pueraria, hyperspectral imaging technology combined with machine learning was used in this experiment to identify the years of Pueraria accurately. However, in fact, one major drawback of this approach is that there is a great deal of redundant information in hyperspectral image data. What is more, the huge amount of data and highly correlated between characteristic bands directly increases the difficulty of sample identification. Principal Component Analysis (PCA) has been taken to extract features from the data to avoid an impact on subsequent classification effects. Based on the full band and PCA dimensionality reduction data to achieve accurate identification of different years of age, there are four classification models currently being adopted in research, including support vector machines (SVM), logistic regression (LR), multi-layer perceptron (MLP) and random forest (Random Forest, RF).When using full-band data modeling, the accuracy of four different classification models under different lenses is 78.09%, 77.03%, 81.43%, 72.09% and 93.11%, 93.79%, 94.23%, 89.77% respectively. The MLP model achieved the best effect under both SN0605VNIR(VNIR) and N3124SWIR(SWIR) lenses. When using PCA dimensionality reduction data modeling, the test set accuracy of four different classification models under two lenses is 96.12%, 87.53%, 95.02%, 93.41% and 99.26%, 97.09%, 99.16%, 97.91% respectively, in which SVM has achieved the optimal prediction accuracy under both VNIR and SWIR lenses. In summary, these results show that the method of PCA can effectively improve the model’s prediction accuracy. In addition, in order to explore the influence of principal component content on prediction accuracy, the authors analyzed the model parameters further, and the experimental results showed that under the VNIR lens, the principal components of the four models accounted for 65%, 75%, 80% and 45% when the accuracy of the test set reached the highest. Under the SWIR lens, when the accuracy of the test set of the four models reached the highest, the proportion of principal components was 20%, 60%, 35% and 30%, respectively. Among them, the PCA-SVM performed the best comprehensive effect, and high prediction accuracy (99.28%) was achieved with 20% principal components. Therefore, the findings of hyperspectral imaging technology combined with machine learning will be of interest to realisingrapid, non-destructive and high-precision identification of the age of Puerariae Thomsonii Radix.