  The structure of heavy nuclei is difficult to disentangle in high-energy
heavy-ion collisions. The deep convolution neural network (DCNN) might be
helpful in mapping the complex final states of heavy-ion collisions to the
nuclear structure in the initial state. Using DCNN for supervised regression,
we successfully extracted the magnitude of the nuclear deformation from
event-by-event correlation between the momentum anisotropy or elliptic flow
($v_2$) and total number of charged hadrons ($dN_{\rm ch}/d\eta$) within a
Monte Carlo model. Furthermore, a degeneracy is found in the correlation
between collisions of prolate-prolate and oblate-oblate nuclei. Using the
Regression Attention Mask algorithm which is designed to interpret what has
been learned by DCNN, we discovered that the correlation in total-overlapped
collisions is sensitive to only large nuclear deformation, while the
correlation in semi-overlapped collisions is discriminative for all magnitudes
of nuclear deformation. The method developed in this study can pave a way for
exploration of other aspects of nuclear structure in heavy-ion collisions.
