  Resonance is instrumental in modern optics and photonics for novel phenomena
such as cavity quantum electrodynamics and electric-field-induced transparency.
While one can use numerical simulations to sweep geometric and material
parameters of optical structures, these simulations usually require
considerably long calculation time (spanning from several hours to several
weeks) and substantial computational resources. Such requirements significantly
limit their applicability in understanding and inverse designing structures
with desired resonance performances. Recently, the introduction of artificial
intelligence allows for faster predictions of resonance with less demanding
computational requirements. However, current end-to-end deep learning
approaches generally fail to predict resonances with high quality-factors
(Q-factor). Here, we introduce a universal deep learning strategy that can
predict ultra-high Q-factor resonances by decomposing spectra with an adaptive
data acquisition (ADA) method while incorporating resonance information. We
exploit bound states in the continuum (BICs) with an infinite Q-factor to
testify this resonance-informed deep learning (RIDL) strategy. The trained RIDL
strategy achieves high-accuracy prediction of reflection spectra and photonic
band structures while using a considerably small training dataset. We further
develop an inverse design algorithm based on the RIDL strategy for a
symmetry-protected BIC on a suspended silicon nitride photonic crystal (PhC)
slab. The predicted and measured angle-resolved band structures show minimum
differences. We expect the RIDL strategy to apply to many other physical
phenomena which exhibit Gaussian, Lorentzian, and Fano resonances.
