Cold-hot nature theory is the core basic theory of traditional Chinese medicine (TCM). "Treating the hot syndrome with cold nature medicine and treating cold syndrome with hot nature medicine" indicates that correct classification of medical properties (cold or hot nature) of Chinese herbal medicines (CHMs) is an important basis for TCM treatment. In this study, we propose a novel multisolvent similarity measure retrieval scheme (MSSMRS) for discriminating CHMs as cold or hot. We explore a multisolvent distance metric learning algorithm to calculate similarity measure of CHM ingredients, and a retrieval scheme for nature identification. First, four solvents (chloroform, distilled water, absolute ethanol, and petroleum ether) are applied to extract ultraviolet (UV) spectrum data of CHM ingredients. Second, we study quantifying the similarity of CHM ingredients to fingerprint similarity. We explore a multisolvent distance metric learning (MSDML) algorithm to measure the similarity of CHM ingredients. MSDML can discover complementary characteristics of different solvent data sets through an optimization algorithm. Finally, a retrieval scheme is designed to analyze the relationship between the CHM ingredients and cold-hot nature. Extensive experimental results demonstrate that CHMs with similar compositions of substances have similar medicinal natures. Experimental evaluations based on the proposed retrieval scheme suggest the effectiveness of MSDML in the identification of the nature of CHMs.