Gancao is a kind of Chinese herbal medicine with the functions of tonifying the spleen and benefiting the qi, clearing away heat and detoxifying toxins, expelling phlegm, relieving cough, etc. Due to the influence of genetics and geography, there is a great difference in the quality and price of different varieties and places of origin, and there are often unscrupulous merchants who use the second-rate to make the best to gain benefits. Aiming at the structural properties of glycyrrhizic acid and glycyrrhizin, which are closely related to the quality of Gancao, this paper constructs a spectral sensing method for the rapid identification of Gancao varieties and origins through six organic dyes and metal ions that can react with them optically in a specific manner. In this paper, 175 samples from five main Gancao production areas, Xinjiang, Inner Mongolia, Ningxia, Beijing, and Uzbekistan, were collected to measure the UV-visible spectral data of Gancao extracts and the addition of different metal ions-organic dyes, respectively. By comparing the differences in UV-visible spectrograms, we have screened out six metal ions and organic dyes that can react with Gancao extracts with specific optical reactions and used them to construct UV-visible sensing points. All the collected samples were then detected and analyzed by the constructed UV-visible spectral sensing method and combined with partial least squares discriminant analysis (PLS-DA) and random forest (RF) algorithms to identify the varieties and origins of Glycyrrhiza glabra. The results showed that the spectral peaks of the original Gancao species and origin overlapped in several places, and the Q2 in the PLS-DA classification results was only 0.75. That is., the pure spectra provided preliminary identification of Gancao but with low predictive accuracy. To further improve its identification accuracy, metal ions, and organic dyes were added to the Gancao extracts to produce spectral peak variations, which were more pronounced for both Gancao species and origin compared to the original UV-Vis spectra of Gancao. Using vector-coded partial least squares discriminant analysis (Dummy codes-PLSDA) and random forest (RF) algorithms to establish identification models of Gancao species and origin, it was found that the accuracies of the fusion spectra of the six sensing materials reached 100%, which significantly improved the recognition ability of the metal ion-organic dye sensing array for Gancao, and the accuracy of the RF model was significantly higher than that of the PLS-DA model, in which the identification accuracies of RF and PLSDA models with a single sensing site were greater than 98.65% and 91.30%, respectively, and much higher than 65.22% of the original spectra. Therefore, in this paper, an ultraviolet-visible spectral sensing method was constructed to accurately identify Gancao species and their origin using metal ions-organic dyes-. The method provides a new idea for rapidly identifying other Chinese herbal medicines, protecting consumers health and interests and promoting sustainable development of Chinese medicine resources.