  Quantum steering has attracted increasing research attention because of its
fundamental importance, as well as its applications in quantum information
science. Here we leverage the power of the deep learning model to infer the
steerability of quantum states with specific numbers of measurement settings,
which form a hierarchical structure. A computational protocol consisting of
iterative tests is constructed to overcome the optimization, meanwhile,
generating the necessary training data. According to the responses of the
well-trained models to the different physics-driven features encoding the
states to be recognized, we can numerically conclude that the most compact
characterization of the Alice-to-Bob steerability is Alice's regularly aligned
steering ellipsoid; whereas Bob's ellipsoid is irrelevant. We have also
provided an explanation to this result with the one-way stochastic local
operations and classical communication. Additionally, our approach is versatile
in revealing further insights into the hierarchical structure of quantum
steering and detecting the hidden steerability.
