Cavitating flows are of great interest in the fields of hydraulic machineries, which can significantly affect mechanical performance and safety. Despite various efforts being dedicated to figuring out the interaction between flow and cavitation fields, their correlation has not been clearly addressed. To this end, in this study, a convolutional neural network, U-Net, was adopted to build a model that can predict the vapor volume fraction from velocity fields. Large eddy simulations of cavitating flows around a Clark-Y hydrofoil were conducted, and the simulated snapshots with velocity and vapor volume fraction were adopted as a dataset for training the network. The predicted vapor volume fraction shows good agreement with the referred simulation results, with a L₁ deviation lower than 2 × 10⁻⁴, considering all the snapshots. The comparable L₁ deviation between the training and validation datasets suggests the existence of a strong correlation between velocity and cavitation fields. The cavitation–velocity interaction derived from using U-Net suggests that the location with zero velocity indicates the interior part of attached and cloud cavitations, and the local vortical velocity fields usually suggest the existence of cavitation shedding.