A direction of arrival (DOA) estimation method based on a convolutional neural network (CNN) using an acoustic vector sensor is proposed to distinguish multiple surface ships in a selected frequency band. The cross-spectrum of the pressure and particle velocity are provided as inputs to the CNN, which is trained using data obtained by employing an acoustic propagation model under different environmental and source parameters. By learning the characteristics of acoustic propagation, the multisource distinguishing performance of the CNN is improved. The proposed method is experimentally validated using real data.