Salt dome identification is of great significance for oil and gas exploration. Many important resources are located near the salt dome, but manual identification is time-consuming, labor-intensive and subjective. To solve this problem, this paper proposes a salt dome identification method based on image segmentation to realize automatic and high-precision identification of salt dome. The method is based on the original U-Net network for migration learning. The SENet loaded with the pre training model is used as the backbone network of the encoder. The salt dome features in the seismic image are enhanced, the important features in the image are highlighted, and the unimportant features are suppressed. In addition, according to the characteristics of salt dome segmentation task, Lovasz Softmax loss function is introduced for standardization experiment to improve the segmentation effect of salt dome boundary. The experimental results on the data set provided by the TGS salt dome identification challenge show that the method has finally achieved 97.5% Accuracy and 87.26% IoU on the test set. Compared with UNet and USKNet, the IoU has increased by 12.59 percentage points and 1.6 percentage points, respectively, reflecting the effectiveness and universality of this method.