Abstract: This paper proposes a deep space-time information network (DSTIN) for the detection of temporal action regions. our method added optical flow information as an input to get the temporal information, uses 3D convolutional networks to get candidate regions and classify the actions. Then our method constructed and trained a specialized 3D convolutional network to detect the state of the candidate regions and perform modification on those regions. Experiment result shows that our method can effectively improve the accuracy of candidate regions for temporal action detection than the existing methods.