  This study investigates the role of large-scale environments on the fraction
of spiral galaxies at $z=$ 0.3-0.6 sliced to three redshift bins of $\Delta
z=0.1$. Here, we sample 276220 massive galaxies in a limited stellar mass of
$5\times10^{10}$ solar mass ($\sim M^\ast$) over 360 deg$^2$, as obtained from
the Second Public Data Release of the Hyper Suprime-Cam Subaru Strategic
Program (HSC-SSP). By combining projected two-dimensional density information
(Shimakawa et al. 2021) and the CAMIRA cluster catalog (Oguri et al. 2018), we
investigate the spiral fraction across large-scale overdensities and in the
vicinity of red sequence clusters. We adopt transfer learning to significantly
reduce the cost of labeling spiral galaxies and then perform stacking analysis
across the entire field to overcome the limitations of sample size. Here we
employ a morphological classification catalog by the Galaxy Zoo Hubble (Willet
et al. 2017) to train the deep learning model. Based on 74103 sources
classified as spirals, we find moderate morphology-density relations on ten
comoving Mpc scale, thanks to the wide-field coverage of HSC-SSP. Clear
deficits of spiral galaxies have also been confirmed, in and around 1136 red
sequence clusters. Furthermore, we verify whether there is a large-scale
environmental dependence on rest-frame $u-r$ colors of spiral galaxies;
however, such a tendency was not observed in our sample.
