  Blazars are among the most studied sources in high-energy astrophysics as
they form the largest fraction of extragalactic gamma-ray sources and are
considered prime candidates for being the counterparts of high-energy
astrophysical neutrinos. Their reliable identification amid the many faint
radio sources is a crucial step for multi-messenger counterpart associations.
As the astronomical community prepares for the coming of a number of new
facilities able to survey the non-thermal sky at unprecedented depths, from
radio to gamma-rays, machine learning techniques for fast and reliable source
identification are ever more relevant. The purpose of this work was to develop
a deep learning architecture to identify blazar within a population of AGN
based solely on non-contemporaneous spectral energy distribution information,
collected from publicly available multi-frequency catalogues. This study uses
an unprecedented amount of data, with SEDs for $\approx 14,000$ sources
collected with the Open Universe VOU-Blazars tool. It uses a convolutional
long-short term memory neural network purposefully built for the problem of SED
classification, which we describe in detail and validate. The network was able
to distinguish blazars from other types of AGNs to a satisfying degree
(achieving a ROC area under curve of $0.98$), even when trained on a reduced
subset of the whole sample. This initial study does not attempt to classify
blazars among their different sub-classes, or quantify the likelihood of any
multi-frequency or multi-messenger association, but is presented as a step
towards these more practically-oriented applications.
