In order to improve the accuracy of subtle fault identification, an artificial intelligence subtle fault prediction method based on the seismic data was developed. By making sample labels based on subtle fault interpretation results, a sample label library based on interpretation results was built. The subtle fault modeling and forward methods for subtle faults were developed, and a label library based on model forward was built. The special neural network for identifying subtle faults was developed, which can directly generate attribute data for subtle faults. In addition, the seismic preprocessing approaches such as removing strong seismic events and structure oriented smoothing filtering were added to improve the original seismic data. Multi-attribute fusion based on principal component was used to reflect multi-scale faults. Finally, the prediction results were verified through three steps, forming the subtle fault prediction workflow with artificial intelligence. This study demonstrates good application in the Daniudi gas field in the Ordos Basin. Compared with conventional attributes, the number of subtle fault identification has increased by 30%, and the resolution and continuity of the subtle faults have significantly improved. The prediction results are consistent with seismic data, well data, and conventional seismic attributes. Based on the subtle faults identification, the new understandings of regional structure are revealed: the dominant orientation of subtle faults is northwest, the most subtle faults are relatively concentrated in the western and northeastern parts of the survey, and the fractures between faults are also very dense. Some subtle faults are distributed along the boundary of the high-quality reservoir, perhaps related to the development of reservoirs. The prediction results contribute to evaluating high-quality reservoirs in the Lower Paleozoic and well deployment, and have application prospects for similar areas.