The invention discloses a video anomaly detection method based on weighted tensor subspace background modeling, which is mainly used for solving the problem that the prior art can not filter out outliers in an image due to the ignorance of space structure information of the image. The implementation process is as follows: firstly regarding training data and observation data as two-order tensors, adopting the tensor analysis method to calculate a projection matrix on each mode, and constituting a tensor subspace; then carrying out robustness analysis on the observation data, weighting each element in the observation data, updating the tensor subspace, projecting the weighted observation data onto the subspace, and reconstructing a background image; and finally carrying out similarity measurement on a reference background and the reconstructed background, and detecting whether an anomalous event happens in a video scene or not. Compared with the prior art, the method can keep the space structure information of the image, filter out the outliers in the image and have robustness. The method can be used for anomalous event detection under the conditions of fixed scenes and slowly-changing illumination in the fields of security protection and monitoring.