Mesoscale eddy is a typical mesoscale ocean phenomenon, which widely exists in all oceans and marginal seas around the world. The spatiotemporal scale of mesoscale eddies ranges from a few days to hundreds of days, tens of kilometers to hundreds of kilometers. The traditional mesoscale eddy identification is subjective and usually depends on expert discrimination or threshold setting. In this study, due to the significant advantages of YOLO series target recognition models in the field of deep learning, we propose an ocean mesoscale eddy identification algorithm for deep transfer learning target recognition based on YOLOF (You Only Look One Level Feature). Compared with traditional recognition methods, this model has better recognition effect, The influence of setting threshold on mesoscale eddy identification is avoided, and the identification speed is improved to a certain extent.