Anomaly detection is an important task in modern industrial manufacturing. Due to the scarcity of abnormal samples, unsupervised detection that only requires normal sample training has attracted widespread attention. Among them, reconstruction based detection has been widely applied due to its concise and universal framework. However, existing algorithms are mostly based on image reconstruction, thus the discrimination between abnormal and normal regions is insufficient. At the same time, due to the strong uncertainty of abnormal positions and sizes in industrial images, existing algorithms cannot capture the overall structural features of samples well. This article proposed an industrial image anomaly detection algorithm based on feature reconstruction to address the above issues. Firstly, the use of pre trained models to extract multi-scale features as reconstruction objects avoids the situation where pixel space reconstruction has insufficient ability to distinguish anomalies; secondly, a global feature extraction module was designed to enhance the perception ability of the reconstruction model towards global features; finally, design a feature recombination strategy to jointly train the reconstruction model, in order to further enhance the model′s understanding of the overall structure of the samples and improve the reconstruction effect. A large number of experiments conducted on MVTec AD have shown that the proposed algorithm achieves an AUROC score of 98.7% in sample level anomaly detection and 98.3% in pixel level anomaly localization, both of which have reached state-of-the-art performance.