Unsupervised anomaly detection methods based on feature embedding often use patch-level features to localize anomalies. Patch-level features are competitive in image-level anomaly detection tasks， but suffer from insufficient accuracy in pixel-level localization. To address this issue， MemAD， a pixel-level anomaly detection method composed of a multi-scale memory bank and a segmentation network， was proposed. Firstly， a pre-trained feature extraction network was used to extract features from normal samples in the training set， thereby constructing a normal sample feature memory bank at three scales. Then， during the training of the segmentation network， difference features between simulated pseudo-anomaly sample features and the nearest normal sample features in the memory bank were calculated， thereby further guiding the segmentation network to learn how to locate anomalous pixels. Experimental results show that MemAD achieves image-level and pixel-level AUC （Area Under the Receiver Operating Characteristic curve） of 0.980 and 0.974 respectively on MVTec AD （MVTec Anomaly Detection） dataset， outperforming most existing methods and confirming the accuracy of the proposed method in pixel-level anomaly localization.