The automatic detection of mesoscale ocean eddies is extremely essential to monitor their dynamic changes. Therefore， effective detection of ocean eddies is vital to improve understanding of ocean dynamics. The traditional methods of detecting eddies using remote sensing data are usually based on physical parameters， geometric features， manual features or expert knowledge. In recent years， the deep learning technology has been improved by many experts. In this paper， the deep learning method is used to detect the ocean eddies from the sea surface height （SSH） data. Firstly， a multi-eddy detection model based on deep convolution neural network is proposed aiming to resolve eddy detection challenge on SSH data photoed by satellite. The model can accurately extract the feature information of eddies and fit the relationship between semantic information and sea level anomaly. Secondly， a new dataset， i.e.， SCSE-Eddy， is used to train the proposed model and evaluate the performance of eddy detection method based on artificial intelligence （AI）. This dataset is composed of the daily satellite remote sensing SSH data covering the South China Sea and its eastern sea area over the past 15 years. Experimental results show that， compared with the existing methods， the model proposed in this paper achieves the best performance and distinguishes close eddies well.