  Detecting the entanglement structure, such as intactness and depth, of an
n-qubit state is important for understanding the imperfectness of the state
preparation in experiments. However, identifying such structure usually
requires an exponential number of local measurements. In this letter, we
propose an efficient machine learning based approach for predicting the
entanglement intactness and depth simultaneously. The generalization ability of
this classifier has been convincingly proved, as it can precisely distinguish
the whole range of pure generalized GHZ states which never exist in the
training process. In particular, the learned classifier can discover the
entanglement intactness and depth bounds for the noised GHZ state, for which
the exact bounds are only partially known.
