With the advancement of DeepFake technology in recent years, the current social platform is full of massive fake videos produced by face-changing technology. Although fake videos can enrich people’s entertainment, they also have disadvantages, such as exposing their personal information. How to accurately detect the fake data generated by DeepFake technology has become an important and difficult task in network security defense. Many researchers have proposed face-changing video detection methods in response to this problem, but the existing detection methods often ignore the incoherence of facial feature crossing video frames. Thus, they are easily countered by optimizing facial synthesizing techniques, resulting in accuracy degradation. Based on this, we propose a novel DeepFake detection method based on long short-term memory (LSTM) network that captures the micro expression changes in terms of the facial features caused by the composite video and uses an encoder to generate features of local visual information. Simultaneously, the attention mechanism is used to achieve the weight distribution of local information. Finally, the LSTM network is used to realize the association information fusion of video frames in temporal space, resulting in the effective detection of DeepFake video data. This paper evaluates a proposed algorithm on the FaceForensics++ dataset, and when compared to existing methods, the experimental results show that the proposed algorithm is superior.