Mesoscale eddy which is of great significance to human activities and marine science is a special mesoscale phenomenon in the ocean. The detection of mesoscale eddies in marine physics usually relies on parameters predefined and adjusted by experts or scanning and judging all ocean data point-by-point. These methods cannot guarantee a satisfied accuracy rate and always take a long time. In addition, the spatio-temporal statistics of mesoscale eddies are complicated, which cannot display relevant information well, and the collation and analysis work is huge. This study proposes an oceanic mesoscale eddy detection algorithm based on deep learning target detection, and it achieves high recognition precision and recall. The proposed algorithm avoids the influence of threshold selection in the methods on mesoscale eddy detection, and greatly improves the detection speed. Meanwhile, we design a visualization system which provides mesoscale eddy space-time features and ocean information. The system can meet the need for insight, description, and correlation analysis of the statistical information, feature distribution, and attribute associations of the mesoscale eddy.