In the field of knowledge distillation (KD), feature-based methods can effectively extract the rich knowledge embedded in the teacher model. However, Logit-based methods often face issues such as insufficient knowledge transfer and low efficiency. Decoupled knowledge distillation (DKD) conducts distillation by dividing the Logits output by the teacher and student models into target and non-target classes. While this method improves distillation accuracy, its single-instance-based distillation approach fails to capture the dynamic relationships among samples within a batch. Especially when there are significant differences in the output distributions of the teacher and student models, relying solely on decoupled distillation cannot effectively bridge these differences. To address the issues inherent in DKD, this study proposes a perception reconstruction method. This method introduces a perception matrix. By utilizing the representational capabilities of the model, it recalibrates Logits, meticulously analyzes intra-class dynamic relationships, and reconstructs finer-grained inter-class relationships. Since the objective of the student model is to minimize representational disparity, this method is extended to decoupled knowledge distillation. The outputs of the teacher and student models are mapped onto the perception matrix, enabling the student model to learn richer knowledge from the teacher model. A series of validations on the CIFAR-100 and ImageNet-1K datasets demonstrate that the student model trained with this method achieves a classification accuracy of 74.98% on the CIFAR-100 dataset, which is 0.87 percentage points higher than that of baseline methods, thereby enhancing the image classification performance of the student model. Additionally, comparative experiments with various methods further verify the superiority of this method.