The invention provides a monitoring video abnormity detection method based on unsupervised learning. According to the method, firstly, a motion block in a video is extracted, then abnormity detectionis carried out from two different angles of local and global, and a detection result is more accurate through diversified detection angles. In local anomaly detection, firstly, a motion block in a video is expanded, then the expanded motion block is used as a basic detection unit, and the difference between the motion block and a neighborhood motion block of the motion block is compared from the time dimension, the space dimension and the space-time dimension; In global anomaly detection, firstly, moving blocks in a video are clustered to extract moving targets, then a sliding window is used on a moving target sequence, the difference between the two moving targets in the window is compared, and finally, a detection result is optimized based on the consistency. The method is suitable for abnormal detection of the monitoring video, low in calculation complexity, accurate in detection result and good in robustness. The method has wide application in the technical field of video analysis.