  The one-dimensional $p$-wave superconductor proposed by Kitaev has long been
a classic example for understanding topological phase transitions through
various methods, such as examining Berry phase, edge states of open chains and,
in particular, aspects from quantum entanglement of ground states. In order to
understand the amount of information carried in the entanglement-related
quantities, here we study topological phase transitions of the model with
emphasis of using the deep learning approach. We feed different quantities,
including Majorana correlation matrices (MCMs), entanglement spectra (ES) or
entanglement eigenvectors (EE) originated from Block correlation matrices
(BCMs), into the deep neural networks for training, and investigate which one
could be the most useful input format in this approach. We find that ES is
indeed too compressed information compared to MCM or EE. MCM and EE can provide
us abundant information to recognize not only the topological phase transitions
in the model but also phases of matter with different $U$(1) gauges, which is
not reachable by using ES only.
