  We investigate the bulk reconstruction of AdS black hole spacetime emergent
from quantum entanglement within a machine learning framework. Utilizing neural
ordinary differential equations alongside Monte-Carlo integration, we develop a
method tailored for continuous training functions to extract the general
isotropic bulk metric from entanglement entropy data. To validate our approach,
we first apply our machine learning algorithm to holographic entanglement
entropy data derived from the Gubser-Rocha and superconductor models, which
serve as representative models of strongly coupled matters in holography. Our
algorithm successfully extracts the corresponding bulk metrics from these data.
Additionally, we extend our methodology to many-body systems by employing
entanglement entropy data from a fermionic tight-binding chain at half filling,
exemplifying critical one-dimensional systems, and derive the associated bulk
metric. We find that the metrics for a tight-binding chain and the Gubser-Rocha
model are similar. We speculate this similarity is due to the metallic property
of these models.
