  Moir\'e superlattice designed in stacked van der Waals material provides a
dynamic platform for hosting exotic and emergent condensed matter phenomena.
However, the relevance of strong correlation effects and the large size of
moir\'e unit cells pose significant challenges for traditional computational
techniques. To overcome these challenges, we develop an unsupervised deep
learning approach to uncover electronic phases emerging from moir\'e systems
based on variational optimization of neural network many-body wavefunction. Our
approach has identified diverse quantum states, including novel phases such as
generalized Wigner crystals, Wigner molecular crystals, and previously
unreported Wigner covalent crystals. These discoveries provide insights into
recent experimental studies and suggest new phases for future exploration. They
also highlight the crucial role of spin polarization in determining Wigner
phases. More importantly, our proposed deep learning approach is proven general
and efficient, offering a powerful framework for studying moir\'e physics.
