According to the fluorescence internal filtering effect (IFE), the more the absorption spectrum of the quencher overlaps with the excitation and emission spectra of the fluorescent substance, the better the quenching effect and, correspondingly, the more significant and sensitive the contrast becomes when the fluorescence is turned on. Thus, in the competitive fluorescence-quenching lateral flow immunoassays (FQ-LFIAs), the fluorescence quencher with an outstanding optical property is of great importance. Herein, gold nanoparticles (AuNPs) and polydopamine (PDA) coengineered covalent organic frameworks (COF/Au@PDA) were synthesized as a fluorescence quencher to increase spectral overlap. Thanks to the excellent visible light absorption of COF with donor-acceptor (D-A) structure, the localized surface plasmon resonance (LSPR) capability of AuNPs, and the broad light absorption of the PDA layer, the COF/Au@PDA exhibits intense absorption and a full spectral overlap toward aggregation-induced emission luminous (AIE) dots. Thereafter, COF/Au@PDA, with its immense potential to completely quench the fluorescence of AIE dots through primary IFE and secondary IFE, was applied to a bimodal LFIA platform for verification with a nitrofurazone metabolite as a model analyte. As expected, the detection sensitivity of the COF/Au@PDA-based FQ-LFIA (turn-on) is improved by 6-fold compared with that of the colorimetric (CM)-LFIA (turn-off). Further, ChatGpt was used to improve the assay accuracy and sensitivity, utilizing its high sensitivity to subtle changes in LFIA signals, especially for weak signals that are indeterminate with the naked eye. This work offers a potential approach for building a high-performance fluorescence quencher in the FQ-LFIA and indicates the potential for the application of artificial intelligence in highly sensitive LFIAs.