The remote sensing satellite can monitor cloud and fog in a large rang e and continuous time,which has great significance for the research of sea fog recognition.In this paper,accor ding to spectral characteristics and texture features of different clouds,fog and underlying surface,relevant featur es are extracted from himawari-8data,and four kinds of sample labels are obtained from CALIOP data,the samples include middle/high clouds, stratus,sea fog and clear sky sea surface.Then deep learning method was applie d to construct deep neural network for achieving effective recognition of sea fog in daytime.The experimental resu lts show that the accuracy of our model is 82.63%,which indicates it has a good performance,and the accuracy is also higher when compares with other sea fog recognition methods.The model is used to recognize sea fog in the Yellow Sea and the Bohai sea on April 8,6.The results through verification using CALIOP data show that the method can recognize sea fog area well.It proves that the method proposed in this paper is feasible.