  Context.Identification of new star cluster candidates in M31 is fundamental
for the study of the M31 stellar cluster system. The machine-learning method
convolutional neural network (CNN) is an efficient algorithm for searching for
new M31 star cluster candidates from tens of millions of images from wide-field
photometric surveys. Aims.We search for new M31 cluster candidates from the
high-quality $g$- and $i$-band images of 21,245,632 sources obtained from the
Pan-Andromeda Archaeological Survey (PAndAS) through a CNN. Methods.We
collected confirmed M31 clusters and noncluster objects from the literature as
our training sample. Accurate double-channel CNNs were constructed and trained
using the training samples. We applied the CNN classification models to the
PAndAS $g$- and $i$-band images of over 21 million sources to search new M31
cluster candidates. The CNN predictions were finally checked by five
experienced human inspectors to obtain high-confidence M31 star cluster
candidates. Results.After the inspection, we identified a catalogue of 117 new
M31 cluster candidates. Most of the new candidates are young clusters that are
located in the M31 disk. Their morphology, colours, and magnitudes are similar
to those of the confirmed young disk clusters. We also identified eight
globular cluster candidates that are located in the M31 halo and exhibit
features similar to those of confirmed halo globular clusters. The projected
distances to the M31 centre for three of them are larger than 100\,kpc.
