  The PandaX dark matter detection project searches for dark matter particles
using the technology of dual phase xenon time projection chamber. The low
expected rate of the signal events makes the control of backgrounds crucial for
the experiment success. In addition to reducing external and internal
backgrounds during the construction and operation of the detector, special
techniques are employed to suppress the background events during the data
analysis. In this article, we demonstrate the use of deep neural
networks(DNNs)for suppressing the accidental backgrounds, as an alternative to
the boosted-decision-tree method used in previous analysis of PandaX-II. A new
data preparation approach is proposed to enhance the stability of the machine
learning algorithms to be run and ultimately the sensitivity of the final data
analysis.
