  Current archives of the LAMOST telescope contain millions of
pipeline-processed spectra that have probably never been seen by human eyes.
Most of the rare objects with interesting physical properties, however, can
only be identified by visual analysis of their characteristic spectral
features. A proper combination of interactive visualisation with modern machine
learning techniques opens new ways to discover such objects. We apply active
learning classification supported by deep convolutional networks to
automatically identify complex emission-line shapes in multi-million spectra
archives.
  We used the pool-based uncertainty sampling active learning driven by a
custom-designed deep convolutional neural network with 12 layers inspired by
VGGNet, AlexNet, and ZFNet, but adapted for one-dimensional feature vectors.
The unlabelled pool set is represented by 4.1 million spectra from the LAMOST
DR2 survey. The initial training of the network was performed on a labelled set
of about 13000 spectra obtained in the region around H$\alpha$ by the 2m Perek
telescope of the Ond\v{r}ejov observatory, which mostly contains spectra of Be
and related early-type stars. The differences between the Ond\v{r}ejov
intermediate-resolution and the LAMOST low-resolution spectrographs were
compensated for by Gaussian blurring.
  After several iterations, the network was able to successfully identify
emission-line stars with an error smaller than 6.5%. Using the technology of
the Virtual Observatory to visualise the results, we discovered 1013 spectra of
948 new candidates of emission-line objects in addition to 664 spectra of 549
objects that are listed in SIMBAD and 2644 spectra of 2291 objects identified
in an earlier paper of a Chinese group led by Wen Hou. The most interesting
objects with unusual spectral properties are discussed in detail.
