Abstract: Coral reef is an important part of the marine ecosystem and is of great significance to the protection of marine biodiversity and the sustainable marine ecological balance. Coral reefs in the South China Sea are full of natural resources. Accurate and efficient extraction of coral reef information is of practical significance for monitoring, management, planning and protection of the reefs in the South China Sea. Based on the remote sensing data of China's HY-1C satellite, the coral reef information of Yongle Islands in Xisha was analyzed, and a coral reef geomorphology classification system was proposed accordingly. The U-Net model of full convolutional neural network was used to extract the geomorphic features of Xisha Yongle Atoll by down-sampling and up-sampling operations in turn to realize the pixel-level semantic segmentation of the original image. The result shows that the geomorphological classification system established based on HY-1C data is indicative of live coral cover and growth conditions. The proposed automatic extraction method of coral reef geomorphological information based on U-Net model can provide corresponding theoretical basis for the fully automatic and large-scale monitoring and coral reef ecosystem evaluation in the South China Sea can play a key role in the ecological management. The verification results show that the adopted geomorphological information extraction method has the ability of spatial and temporal generalization accuracy higher than 80%.