Fault interpretation is one of the basic works of oil & gas exploration and development. In recent years，with its powerful data analysis ability，deep learning has provided a new technical tool for characterizing the spatial distribution of faults in detail. Its application relies on how to obtain a large number of reliable sample data. Compared with the most popular fault data from forward modeling samples at present，fault data from expert interpretation samples of actual data are not only subjective but also focus on target areas，with incomplete labels of fault samples. The improved loss function of incomplete labels of fault samples emphasizes interpreted areas in fault analysis，which improves the availability of fault data from expert interpretation samples. Through the enhancement processing of data from expert fault interpretation samples，the amount of effective sample data is increased. In addition，this paper designs and constructs a network structure of an automatic 3D fault identification method driven by data from expert interpretation samples and forward modeling samples，and it introduces a self-attention mechanism to improve the generalization ability and spatial feature analysis ability of the automatic 3D fault identification network model. Model tests and practical application show that the proposed automatic 3D fault identification method can analyze actual features of the fault development. The identification results are more in line with geological conditions and the accuracy is effectively improved，which verifies the reliability and practicality of this method.