  We discuss and demonstrate an unsupervised machine-learning procedure to
detect topological order in quantum many-body systems. Using a restricted
Boltzmann machine to define a variational ansatz for the low-energy spectrum,
we sample wave functions with probability decaying exponentially with their
variational energy; this defines our training dataset that we use as input to a
diffusion map scheme. The diffusion map provides a low-dimensional embedding of
the wave functions, revealing the presence or absence of superselection sectors
and, thus, topological order. We show that for the diffusion map, the required
similarity measure of quantum states can be defined in terms of the network
parameters, allowing for an efficient evaluation within polynomial time.
However, possible ''gauge redundancies'' have to be carefully taken into
account. As an explicit example, we apply the method to the toric code.
