This paper proposed a deep learning model based on a semantic segmentation neural network (UNet) for extracting floating macroalgae blooms effectively from the data of Geostationary Ocean Color Imager (GOCI) satellite sensors, achieving the end-to-end and pixel-to-pixel segmentation and recognition of the information of floating macroalgae blooms. The validation results show that the average recognition accuracy of the deep learning model for floating macroalgae blooms in the validation set can reach 88.54%. Compared with existing methods for detecting floating macroalgae blooms, including normalized difference vegetation index (NDVI) and alternative floating algae index (AFAI), the constructed model based on the UNet for monitoring floating macroalgae blooms has high accuracy and is less affected by clouds. Consequently, the recognition results of the UNet based model for floating macroalgae blooms are successfully applied to analyzing the outbreak process of floating macroalgae blooms in the East China Sea in 2017. The proposed model indicates a good applicability.