  With the enhancement of sensitivity of Gravitational Wave (GW) detectors and
capabilities of large survey facilities, such as Vera Rubin Observatory Legacy
Survey of Space and Time (LSST) and 2.5-m Wide Field Survey Telescope (WFST),
we now have the potential to detect an increasing number of distant kilonova
(KN). However, distinguishing KN from the plethora of detected transients in
ongoing and future follow-up surveys presents a significant challenge. In this
study, our objective is to establish an efficient classification mechanism
tailored for the follow-up survey conducted by WFST, with a specific focus on
identifying KN associated with GW. We employ a novel temporal convolutional
neural network architecture, trained using simulated multi-band photometry
lasting for 3 days by WFST, accompanied by contextual information, i.e.
luminosity distance information by GW. By comparison of the choices of
contextual information, we can reach 95\% precision, and 94\% recall for our
best model. It also performs good validation on photometry data on AT2017gfo
and AT2019npv. Furthermore, we investigate the ability of the model to
distinguish KN in a GW follow-up survey. We conclude that there is over 80\%
probability that we can capture true KN in selected 20 candidates among $\sim
250$ detected astrophysical transients that have passed real-bogus filter and
cross-matching.
