  Pulsar searching is essential for the scientific research in the field of
physics and astrophysics. As the development of the radio telescope, the
exploding volume and it growth speed of candidates growth have brought about
several challenges. Therefore, there is an urgent demand for developing an
automatic, accurate and efficient pulsar candidate selection method. To meet
this need, this work designed a Concat Convolutional Neural Network (CCNN) to
identify the candidates collected from the Five-hundred-meter Aperture
Spherical Telescope (FAST) data. The CCNN extracts some "pulsar-like" patterns
from the diagnostic subplots using Convolutional Neural Network (CNN) and
combines these CNN features by a concatenate layer. Therefore, the CCNN is an
end-to-end learning model without any need for any intermediate labels, which
makes CCNN suitable for the online learning pipeline of pulsar candidate
selection. Experimental results on FAST data show that the CCNN outperforms the
available state-of-the-art models in similar scenario. It only misses 4 real
pulsars out of 326 totally.
