The processing procedure of traditional Chinese herbal medicines (CHMs) plays an essential role in clinical applications. However, little progress has been made on the quality control of crude and processed products. The present work, taking Radix Scutellariae (RS), wine-processed RS and carbonized RS as a typical case, developed a comprehensive strategy integrating chromatographic analysis and chemometric methods for quality evaluation and discrimination of crude RS and its processed products. Chemical fingerprints were established by high-performance liquid chromatography coupled with photodiode array detector and quadrupole time-of-flight mass spectrometry, and similarity analyses were calculated based on eleven common characteristic peaks. Subsequently, four chemical markers were discovered by back propagation-artificial neural network (BP-ANN) modeling. The selected markers were quantified by the ‘single standard to determine multi-components’ (SSDMC) method, and then the quantitative data were subjected to principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Furthermore, support vector machine (SVM) was employed to predict the different processed products of RS. Finally, a hotmap visualization was conducted for clarifying the distribution of major flavonoids among different drugs. Collectively, the proposed strategy might be well-acceptable for quality control of CHMs and their related processed products from the processing mechanism-based perspective.