On the basis of the convolutional neural network（CNN）of deep learning theory，the calculation methods for the absolute permeability of sandstone digital cores and related important influencing factors were discussed. The study selected three representative sandstone samples，including digital cores of Bentheimer sandstone，Berea sandstone，and Doddington sandstone. First，the permeability of these samples was calculated through the N-S equation and the pore network model separately，and the difference was compared. Then，we explored the influence of three different subsample sizes on the mean absolute permeability and permeability components in different directions when the sandstone samples were cut into subsamples. On this basis，the original sandstone digital cores were cut by the size of 200×200×200 to obtain subsamples，and all subsamples were subjected to microscopic seepage simulation and calculations to produce the corresponding absolute permeability. Finally，the digital core subsample database for deep learning was constructed. Given this database，we discussed the selection methods of key parameters for CNN system construction，such as learning rates and dropout rates.Upon training and learning，the subsamples of the testing set were tested，and the difference between the predicted values and the true values was within 5%，which proves the effectiveness of the method.