Unsupervised anomaly detection methods have made significant advancements in addressing real-world industrial anomaly detection tasks. Among these, feature reconstruction-based approaches have shown exceptional performance, particularly in terms of accuracy and real-time processing capabilities. However, in the more practical multi-class anomaly detection scenarios, these methods may fall into an “identical shortcut”, where the model simply returns a copy of the input, resulting in the anomaly features being effectively reconstructed as well. To overcome this, we propose a Memory-guided Hierarchical Feature Reconstruction method for Multi-class Unsupervised Anomaly Detection. Firstly, we employ a Memory-guided Feature Alignment (MFA) module to align deep features of normal samples, preventing the “identical shortcut” problem and avoiding the reconstruction of anomalous features. Secondly, we introduce a Position-Aware Spatial Attention (PASA) mechanism to compensate for the loss of positional information in the shallow decoder, enabling improved hierarchical feature reconstruction. We validated the effectiveness of our approach on the MVTec and MVTec LOCO datasets, achieving AUROC scores of 98.6% and 84.2%, respectively, surpassing state-of-the-art methods.