  TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino
telescope to be located in the South China Sea. With a large detector volume
and the use of advanced hybrid digital optical modules (hDOMs), TRIDENT aims to
discover multiple astrophysical neutrino sources and probe all-flavor neutrino
physics. The reconstruction resolution of primary neutrinos is on the critical
path to these scientific goals. We have developed a novel reconstruction method
based on graph neural network (GNN) for TRIDENT. In this paper, we present the
reconstruction performance of the GNN-based approach on both track- and
shower-like neutrino events in TRIDENT.
