Marine mesoscale eddies are a common marine ocean phenomenon. Identifying mesoscale eddies can play an important role in shipping, military, and ocean resource development. Compared to traditional recognition algorithms, artificial intelligence methods can identify mesoscale eddies more accurately and efficiently. An intelligent detection algorithm with loss function gradient optimization was used to realize identification of mesoscale eddies. Firstly, the mesoscale eddy dataset was built from the satellite altimeter remote sensing data and the global ocean physics reanalysis data provided by the Copernicus Marine Environment Monitoring Service (CMEMS), and the PET method and Shapely tools were applied to generate labels for the dataset which would be used in intelligent models. Then, the U-Net AI model was realized to identify the mesoscale eddies. In this model, an attention mechanism for multi sources and fusion of features on each layer were added to optimize model performance. Finally, a loss function gradient optimization was introduced to make the model more consistent with the mesoscale eddies. By introducing 60% loss function gradient constraint weights in the model, the accuracy can be improved from 93.57% to 94.46%. The result show that multi source data, fusion of features and loss function gradient optimization constraints significantly improve the ability of artificial intelligence methods to recognize mesoscale eddies.