Abstract: To meet the needs of abnormal behavior detection for large-scale video data, methods based on video frame reconstruction and frame prediction have been widely studied. However, because the background environment is almost constant under the monitoring perspective, a lot of resources will be wasted on the constant background, and it is also not conducive to extracting the detection target information. In order to solve this problem, this paper uses an unsupervised learning video frame prediction strategy, and uses generative adversarial networks to learn features of normal behavior to generate better predicted frames. And the attention-driven loss is used to alleviate the problem of the imbalance between the foreground target and the background environment in abnormal behavior detection, and the spatial-channel attention mechanism (CBAM) is used to enhance the prediction effect of the model generator.After the test and verification of public data sets UCSD Ped1 and UCSD Ped2, the detection accuracy on the Ped1 dataset has reached 83.5%, and the detection accuracy on the Ped2 dataset has reached 95.8%.And compared with the classic abnormal behavior detection algorithm and the original generative adversarial network based anomaly detection algorithm, the method adopted in this paper further improves the accuracy of abnormal behavior detection.