Abstract: Detection and identification of faults are crucial in the process of coal exploration and mining, and the traditional manual method of fault interpretation can no longer meet the needs of actual production, and the deep learning-based seismic fault interpretation method performs better in the field of fault segmentation. Conventional convolutional neural network (CNN) has limited sensory field and cannot make good use of the global information, which will lead to some predicted faults with insufficient continuity and missing faults, etc. Transformer has the advantage of extracting global information, and introduces the TransUNet network which is a fusion of CNN and Transformer to construct a CBAM- based seismic fault identification method. TransUNet seismic fault identification method to identify 2D seismic fault images. Firstly, the CBAM-Block attention module is integrated into the TransUNet network, and the module is added into the CNN tomography encoder part and the 3-layer jump connection part connecting the tomography encoder and the tomography decoder, respectively, to enhance the recognition ability of the seismic tomography image from two dimensions, namely, the channel and the space; secondly, the loss function optimised jointly by the Dice loss function and the cross-entropy loss function is selected to make the segmentation of the tomography image more accurate. function and cross-entropy loss function to make the fault image segmentation more accurate, and the DICE and IOU values obtained by the CBAM-TransUNet fault identification network on the synthetic seismic dataset are increased to 0.84 and 0.75, respectively, and the experimental results show that the continuity of the fault identification is stronger, which is obviously superior to other classical segmentation methods; finally, the constructed model is used to interpret the faults on the real seismic dataset of the F3 block of the North Sea, off the coast of the Netherlands. Finally, the constructed model was used to interpret the faults in the real seismic data set of Block F3 in the North Sea off the Netherlands. The experimental results show that the seismic fault identification method based on CBAM-TransUNet can effectively identify the faults while removing the redundant fault information, and performs well in terms of fault identification accuracy and fault identification continuity, and the identified faults are richer in details, which improves the accuracy of fault identification, and can be effectively applied to identify the faults in the real seismic data.