The theory of cold–hot nature of Chinese herbal medicines (CHMs) is the core theory of CHM. It has been found that the volatile oil ingredients in CHMs are closely related to their cold–hot nature. Guided by the scientific hypothesis that “CHMs with similar component substances should have similar medicinal natures”, exploration of the intelligent identification of the cold–hot nature of CHMs based on the similarity of their volatile oil ingredients has become a research focus. Gas chromatography (GC) chemical fingerprints have been widely used in the separation of volatile oil ingredients to analyze the cold–hot nature of CHMs. To verify the above hypothesis, in this work, we study the quantification of the similarity of the volatile oil ingredients of CHMs to their fingerprint similarity and explore the relationship between the volatile oil ingredients of CHMs and their cold–hot nature. In this study, we utilize GC technology to analyze the chemical ingredients of 61 CHMs that have a clear cold–hot nature (including 30 ‘cold’ CHMs and 31 ‘hot’ CHMs). Using the constructed fingerprint dataset of CHMs, a distance metric learning algorithm is applied to measure the similarity of the GC fingerprints. Furthermore, an improved k-nearest neighbor (kNN) algorithm is proposed to build a predictive identification model to identify the cold–hot nature of CHMs. The experimental results prove our inference that CHMs with similar component substances should have similar medicinal natures. Compared with existing classical models, the proposed identification scheme has better predictive performance. The proposed prediction model is proved to be effective and feasible.