Abstract: Aiming at the problems of diverse types of marine mariculture areas (raft mariculture, reclamation mariculture, cage mariculture, etc.), small feature differences and the difficulty in achieving high-precision classification extraction, this paper integrates atrous spatial pyramid convolution module into the U-Net neural network model and proposes a multi-classification extraction method suitable for mariculture areas. Firstly, variogram analysis is used to find the difference between waveform and sill value of variogram of different types of mariculture areas. Secondly, define a parallel convolution structure of atrous convolutions with different atrous rates similar to the variogram search domain and replace the ordinary convolution structure in the U-Net model with it to construct the ASP-U-Net model. Then, in order to verify the ability to classify and extract mariculture areas using the ASP-U-Net model, 7 scenes of domestic Gaofen 1/2 satellite images are selected as data sources. Compared with the classic FCN, SegNet, PspNet and classic U-Net models, the ASP-U-Net model has the best performance among them in classification and extraction of marine mariculture areas under various indicators. It shows that using the proposed convolution structure can expand the receptive field effectively and is more suitable for the expression of characteristics in multi-type mariculture areas.