As the core link of intelligent manufacturing quality control, the detection accuracy and real-time performance of surface defects in industrial products are crucial for industrial production. Aiming at the key problems of insufficient local feature sensitivity and high computational redundancy faced by existing unsupervised anomaly detection methods in complex industrial scenarios, an improved multi-scale feature fusion detection algorithm based on PatchCore is proposed. Firstly, by introducing a multi-scale feature fusion processing method with self attention mechanism, the layer 3 feature map is fused with self attention mechanism and average pooling to enhance the algorithm′s ability to capture local and global abnormal features; propose a channel aggregation dimensionality reduction method, which randomly divides the original features into several continuous subgroups and aggregates each group of features to generate low dimensional features, achieving the goal of reducing computational redundancy while preserving some local information of the original features; build transfer learning models to enhance the algorithm′s generalization ability in anomaly detection tasks and improve the detection accuracy of actual industrial projects. Through defect detection experiments on memory heat sink images, the results show that the improved algorithm improves AUROC by 2.28% and F1Score by 4.89% compared to the original algorithm, which can meet the requirements of high efficiency and high precision in industrial scenarios.