We have applied recent machine learning advances-deep convolutional neural networks-to three-dimensional (voxels) soft matter data, generated by molecular dynamics computer simulation. We have focused on the structural and phase properties of a coarse-grained model of hydrated ionic surfactants. We have trained a classifier able to automatically detect the water quantity absorbed in the system, therefore associating to each hydration level the corresponding most representative nanostructure. On the basis of the notion of transfer learning, we have next applied the same network to the related polymeric ionomer Nafion and have extracted a measure of the similarity of these configurations with those above. We demonstrate that on this basis it is possible to express the static structure factor of the polymer at fixed hydration level as a superposition of those of the surfactants at multiple water contents. We suggest that such a procedure can provide a useful, agnostic, data-driven, and precise description of the multiscale structure of disordered materials, without resorting to any a priori model picture.