The fault interpretation is a crucial step in oil and gas exploration and development. Due to the increase in the number of collected 3D seismic data volumes，the manual and traditional methods can hardly interpret faults in data volumes in detail. To better meet the urgent needs for high-efficiency，high-precision，and high-resolution fault interpretation in oil and gas exploration and development，we propose a deep learning-based algorithm to realize the automatic and intelligent fault detection with seismic data. The forward modeling method is used to generate a large number of diversified training data in line with the actual situation，and at the same time，a complete training sample volume is constructed in combination with the existing fault interpretation results. On this basis，an optimized and simple three-dimensional（3D）convolutional neural network（CNN）model is designed to efficiently process large 3D seismic data volumes and obtain accurate fault detection results. We further apply scan processing of matched filtering to the fault detection results to enhance the fault probability volumes and at the same time，obtain an estimation of fault strikes and dips. Given the three fault attribute volumes，we finally utilize a region-growing algorithm to automatically construct all the fault surfaces in the data volumes.Compared with the conventional methods commonly used in the industry，our method is significantly superior to the conventional methods in robustness to noise，accuracy，and efficiency.