  The cosmic distance duality relation (DDR) is constrained from the
combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing
(SGL) systems using deep learning method. To make use of the full SGL data, we
reconstruct the luminosity distance from SNe Ia up to the highest redshift of
SGL using deep learning, then it is compared with the angular diameter distance
obtained from SGL. Considering the influence of lens mass profile, we constrain
the possible violation of DDR in three lens mass models. Results show that in
the SIS model and EPL model, DDR is violated at high confidence level, with the
violation parameter $\eta_0=-0.193^{+0.021}_{-0.019}$ and
$\eta_0=-0.247^{+0.014}_{-0.013}$, respectively. In the PL model, however, DDR
is verified within 1$\sigma$ confidence level, with the violation parameter
$\eta_0=-0.014^{+0.053}_{-0.045}$. Our results demonstrate that the constraints
on DDR strongly depend on the lens mass models. Given a specific lens mass
model, DDR can be constrained at a precision of $\textit{O}(10^{-2})$ using
deep learning.
