Industrial products consist of multiple structural components. Assembly anomalies—such as foreign objects, incorrect connections, missing parts, and misplacements—can severely affect performance and safety. Unlike structural defects in surface inspection, assembly anomalies are often logical, caused by component deformation, occlusion, or varying constraints, making traditional local-consistency-based methods less suitable. This paper proposes an anomaly detection method for assembly scenarios that handles both logical and structural anomalies. The method first constructs a memory-indexed histogram descriptor based on multi-representative feature replacement, which statistically encodes the index distribution of reconstructed local features to enhance the modeling of positional, connective, and quantitative constraints. Second, it introduces a graph-attention-enhanced global feature memory bank, which employs a neighbor-graph structure and an attention mechanism to model cross-region topological dependencies and represent large-scale spatial structures. During testing, a two-stage matching process between the histogram memory and the global memory enables accurate detection of logical anomalies including missing, misconnected, and foreign objects, while maintaining competitive performance on structural anomalies. Experiments on several industrial and public datasets demonstrate the method’s effectiveness. On the battery-pack dataset Fuse-Cable, it achieves 99.86% image‑level AU‑ROC and 93.78% pixel‑level AU‑sPRO. The results show that the proposed method outperforms existing local‑embedding approaches in both recognition and localization, exhibiting good generalization, robustness, and practical value.