Objective: To establish a multi-component determination method and HPLC fingerprint for Vernonia amygdalina Del., combined with chemical pattern recognition to comprehensively evaluate the quality of Vernonia amygdalina Del. from different producing, laying a foundation for the future development of quality control standards for Vernonia amygdalina Del. and other related research on Vernonia amygdalina Del.’s components. Methods: The HPLC using Welch Ultimate^® AQ-C₁₈ column (250 mm×4.6 mm,5 µm) and 0.2% phosphoric acid aqueous solution - acetonitrile as moblie phase by gradient elution at the flow rate of 1 mL · min^-1, wavelength was set at 258 nm and the column temperature was 35 ℃. Simultaneously establish 24 batches of fingerprint spectra of Vernonia amygdalina Del. from different origins and a multi-component HPLC determination method for 9 components of Vernonia amygdalina Del.. Combine chemical pattern recognition to evaluate the quality of 24 batches of Vernonia amygdalina Del. from different origins. Results: The similarities of the fingerprint spectra of 24 batches of Vernonia amygdalina Del. were above 0.9. The established control fingerprint spectra could stably and effectively distinguish Vernonia amygdalina Del. qualitatively. There were 10 common peaks in the fingerprint spectra, and 9 peaks were identified. The mass fractions of 24 batches of Vernonia amygdalina Del. components that were uracil, chlorogenic acid, luteolin, luteolin-7-O-β-D-glucuronide, isochlorogenic acid B, 3,5-O-dicaffeoyl quinic acid, 1,5-dicaffeoyl quinic acid, apigenin-7-O-β-D-glucopyranoside, 4,5-O-dicaffeoyl quinic acid were 0.007 314-0.084 30 mg · g^-1,0.619 6-9.763 mg · g^-1,0.303 8-4.031 mg · g^-1,0.984 6-8.146 mg · g^-1,0.043 29-0.438 7 mg · g^-1,0.537 5-11.57 mg · g^-1,0.437 6-13.78 mg · g^-1,0.032 19-0.720 1 mg · g^-1,0.190 0-1.931 mg · g^-1. Through cluster analysis, 24 batches of Vernonia amygdalina Del. were divided into 2 categories. Vernonia amygdalina Del. from Guangdong and Hainan were classified as one category, while Vernonia amygdalina Del. from Guangxi were classified as one category alone. The classification results of principal component analysis were consistent with cluster analysis. Further research on the differences in leaf quality among different regions of Vernonia amygdalina Del. was conducted through principal component scores, and it was found that the P5, P24 and P22 regions ranked in the top three with better quality than other regions. Through orthogonal partial least squares discriminant analysis, it was found that the reasons for the differences in leaf quality among different regions of Vernonia amygdalina Del. were ranked in descending order: 1,5-dicaffeoyl quinic acid, chlorogenic acid, and luteolin-7-O-β-D-glucuronide, 3,5-O-dicaffeoyl quinic acid. Conclusion: The fingerprint and multi-component determination methods of Vernonia amygdalina Del. are stable and effective through methodological detection, which can fill the gap in quality control standards for Vernonia amygdalina Del.. Combined with chemical pattern recognition methods, the high-quality production areas of Vernonia amygdalina Del. and the components that should be emphasized in controlling the quality of Vernonia amygdalina Del. from different production areas are pointed out. A comprehensive evaluation of the quality of Vernonia amygdalina Del. has been conducted, providing a solid foundation for future quality control and evaluation of Vernonia amygdalina Del..