LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as glitches, which often mimic or overlap with gravitational wave (GW) signals. Given the higher candidate event rate in the current observing run (O4) compared to previous observing runs, the process of vetting GW candidates, including identifying glitches responsible for or overlapping with candidates, requires more automation than in previous runs. We present GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN multi-label classifier based on GravitySpy. Achieving an accuracy surpassing 96%, GSpyNetTree identifies glitches present in each GW detector at the time of a signal candidate. We have trained GSpyNetTree to be robust to a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. We evaluate the performance of GSpyNetTree, report on its use in the O4 LIGO-Virgo event validation process, and suggest novel strategies to improve classifications.