To address the issue of knowledge redundancy in knowledge distillation-based anomaly detection models, this paper proposes an industrial detection method based on asymptotic knowledge fusion distillation. This paper adopts reverse knowledge distillation as the backbone network of the detection model. Although reverse knowledge distillation can prevent the propagation of abnormal representations to the student network, the knowledge acquired by the student network under this method is not only complex but also redundant, making it difficult to ensure that the student network can reconstruct the corresponding shallow representations. To transform the high-level, complex, and redundant information output by the teacher network, an asymptotic knowledge fusion mechanism is proposed. For transforming complex representations, this mechanism gradually transfers basic geometric knowledge to deep semantic knowledge, enabling the student network to learn feature representations effectively. For eliminating redundant knowledge from the information output by multi-level teacher networks, this mechanism adopts a learnable feature weight assignment method, which promotes the reconstruction of the student network and improves the anomaly detection capability of the model. Experiments are conducted on the MVTec AD dataset. The results show that the evaluation metrics under all categories of the dataset are 99% for AUROC-image, 97.9% for AUROC-pixel, and 94.8% for AUPRO, which outperform most of the current mainstream detection models, verifying the effectiveness and superiority of the proposed method.