Although serving as the workhorse, MS/MS cannot fully satisfy the analytical requirements of quantitative sub-metabolome characterization. Because more information intrinsically correlates to more structural and concentration clues, here, efforts were devoted to comprehensively tracing and deciphering MS/MS behaviors through constructing triple three-dimensional (3×3D)-MS/MS spectrum. Ginsenosides-targeted metabolomics of notoginseng, one of the most famous edible medicinal plants, was employed as a proof-of-concept. Serial authentic ginsenosides were deployed to build the correlations between 3×3D-MS/MS spectra and structure/concentration features. Through assaying ginsenosides with progressive concentrations using QTOF-MS to configure 1 3D spectrum, the generations of MS spectral signals, particularly multi-charged multimer anions, e.g., [2M–2H] and [2M+2HCOO] ions, relied on both concentration and the amount of sugar chains. By programming progressive collision energies to the front collision cell of Qtrap-MS device to gain 2 3D spectrum, optimal collision energy (OCE) corresponding to the glycosidic bond fission was primarily correlated with the masses of precursor and fragment ions and partially governed by the glycosidation site. The quantitative relationships between OCEs and masses of precursor and fragment ions were utilized to build large-scale quantitative program for ginsenosides. After applying progressive exciting energies to the back collision chamber to build 3 3D spectrum, the fragment ion and the decomposition product anion exhibited identical dissociation trajectories when they shared the same molecular geometry. After ginsenosides-focused quantitative metabolomics, significant differences occurred for sub-metabolome amongst different parts of notoginseng. The differential ginsenosides were confirmatively identified by applying the correlations between 3×3D-MS/MS spectra and structures. Together, 3×3D-MS/MS spectrum covers all MS/MS behaviors and dramatically facilitates sub-metabolome characterization from both quantitative program development and structural identification.