The engineering naming of marine sediments plays an important role in the development of marine engineering construction. However, the naming of silt and clay in the seabed is easy to be affected by human factors. Using the artificial neural network method, this study trained 284 sets of finegrained soil data in Chengdao sea area of the Yellow River estuary and proposed a naming method using granularity data. Results showed that the method of artificial neural network performs well in fine submarine soil engineering naming. The accuracy of net naming is the highest, which as high as 97.7%, when the network contains 5 input layer nodes, 9 hidden layer nodes, 3 output layer nodes, and scaled conjugate gradient as the training function. The number of training data is an important factor that causes errors in neural network prediction. With increasing data volume, the reliability and universality of the network will become higher and higher.