The coastal zone is the most active natural area on the Earth’s surface and has the most favorable resources and environmental conditions. Therefore, it is of great significance to conduct research based on the coastal zone. Hyperspectral remote sensing images have spatial and spectral dimensions that reflect the spatial distribution and can analyze the compositional information, which has been widely used for feature analysis and observation of ground objects. In this article, we propose a coastal zone extraction algorithm based on multilayer depth features for hyperspectral images (HSIs). The main contributions are as follows: 1) the Huanjing satellite hyperspectral coastal zone database is built for the first time, image composition is analyzed, and the noise removal algorithm is yielded; 2) 3-D attention networks that are capable of carrying spatial and interspectral information are proposed; and 3) A 3-D convolutional neural network (CNN) with squeeze and excitation network (SENet) tandem structure is proposed to fully exploit detailed information, and a multilayer feature extraction framework is built. We analyze four typical coastal zone patterns, and the experimental results show that our proposed algorithm can achieve coastal zone extraction with an average Kappa coefficient of 0.92, which is 0.06 higher than the mainstream algorithms. Our algorithm also shows good performance in complex environments. It provides a basis for further research on coastal zones.