The photochemical reactivity of humic substances plays a critical role in the global carbon cycle, and influences the toxicity, mobility, and bioavailability of contaminants by altering their molecular structure and the mineralization of organic carbon to CO2. Here, we examined the simulated irradiation process of Chinese standard fulvic acid (FA) and humic acid (HA) by using excitation-emission matrix fluorescence combined with fluorescence regional integration (FRI), parallel factor (PARAFAC) analysis, and kinetic models. Humic-like and fulvic-like materials were the main materials (constituting more than 90%) of both FA and HA, according to the FRI analysis. Four components were identified by the PARAFAC analysis: fulvic-like components composed of both carboxylic-like and phenolic-like chromophores (C1), terrestrial humic-like components primarily composed of carboxylic-like chromophores (C2), microbial humic-like overwhelming composed of phenolic-like fluorophores (C3), and protein-like components (C4). After irradiation for 72 h, the maximum fluorescence intensity (Fmax) of C1 and C2 of FA was reduced to 36.01–58.34%, while the Fmax of C3 of both FA and HA also decreased to 0–9.63%. By contrast, for HA, the Fmax of its C1 and C2 increased to 236.18–294.77% when irradiated for 72 h due to greater aromaticity and photorefractive tendencies. The first-order kinetic model (R2 = 0.908–0.990) fitted better than zero-order kinetic model (R2 = 0–0.754) for the C1, C2, and C3, of both FA and HA, during their photochemical reactivity. The photodegradation rate constant (k1) of C1 had values (0.105 for FA; 0.154 for HA) that surpassed those of C2 (0.059 for FA, 0.079 for HA) and C3 (0.079 for both FA and HA) based on the first-order kinetic model. The half-life times of C1, C2, and C3 ranged from 6.61–11.77 h to 4.50–8.81 h for FA and HA, respectively. Combining an excitation-emission matrix with FRI and PARAFAC analyses is a powerful approach for elucidating changes to humic substances during their irradiation, which is helpful for predicting the environmental toxicity of contaminants in natural ecosystems.