Workpiece anomaly detection is a key link in production. Due to the small number of abnormal samples and large randomness, supervised learning can not fully learn all types of anomalies, and there exists the problem of poor model stability. In order to solve the above problems, this paper studies an unsupervised workpiece anomaly detection algorithm based on reverse knowledge distillation, and uses the teacher model and student model designed by ResNet network structure as the backbone network. The teacher model truly extracts the image features, the student model reconstructs the image according to the prior knowledge, and adopts the reverse structure to expand the specificity of the abnormal condition. A memory module and a mask attention module are added to extract the multi-dimensional feature information of the sample to avoid missing the details of the image; after the memory module, a mask attention mechanism is added to integrate the multi-dimensional and multi-level features of the image. the accuracy of detection is further improved. The experimental results on two open industrial anomaly detection data sets show that the proposed algorithm is 5% higher than the general knowledge distillation algorithm AUC by 7%, and the effect of locating subtle anomalies is better.