  Entanglement constitutes a key characteristic feature of quantum matter. Its
detection, however, still faces major challenges. In this letter, we formulate
a framework for probing entanglement based on machine learning techniques. The
central element is a protocol for the generation of statistical images from
quantum many-body states, with which we perform image classification by means
of convolutional neural networks. We show that the resulting quantum
entanglement recognition task is accurate and can be assigned a well-controlled
error across a wide range of quantum states. We discuss the potential use of
our scheme to quantify quantum entanglement in experiments. Our developed
scheme provides a generally applicable strategy for quantum entanglement
recognition in both equilibrium and nonequilibrium quantum matter.
