  Motivated by the close relations of the renormalization group with both the
holography duality and the deep learning, we propose that the holographic
geometry can emerge from deep learning the entanglement feature of a quantum
many-body state. We develop a concrete algorithm, call the entanglement feature
learning (EFL), based on the random tensor network (RTN) model for the tensor
network holography. We show that each RTN can be mapped to a Boltzmann machine,
trained by the entanglement entropies over all subregions of a given quantum
many-body state. The goal is to construct the optimal RTN that best reproduce
the entanglement feature. The RTN geometry can then be interpreted as the
emergent holographic geometry. We demonstrate the EFL algorithm on 1D free
fermion system and observe the emergence of the hyperbolic geometry (AdS$_3$
spatial geometry) as we tune the fermion system towards the gapless critical
point (CFT$_2$ point).
