  The Jiangmen Underground Neutrino Observatory (JUNO) is designed to determine
the neutrino mass ordering and measure neutrino oscillation parameters. A
precise muon reconstruction is crucial to reduce one of the major backgrounds
induced by cosmic muons. This article proposes a novel muon reconstruction
method based on convolutional neural network (CNN) models. In this method, the
track information reconstructed by the top tracker is used for network
training. The training dataset is augmented by applying a rotation to muon
tracks to compensate for the limited angular coverage of the top tracker. The
muon reconstruction with the CNN model can produce unbiased tracks with
performance that spatial resolution is better than 10 cm and angular resolution
is better than 0.6 degrees. By using a GPU accelerated implementation a speedup
factor of 100 compared to existing CPU techniques has been demonstrated.
