The temperature in knowledge distillation (KD) is set as a fixed value during the distillation process in most previous work. However, when the temperature is reexamined, it is found that the fixed temperature restricts inherent knowledge utilization in each sample. This study divides the dataset into low-energy and high-energy samples based on energy scores. Through experiments, it is confirmed that the confidence score of low-energy samples is high, indicating that predictions are deterministic, while the confidence score of high-energy samples is low, indicating that predictions are uncertain. To extract the best knowledge by adjusting non-target class predictions, this study applies higher temperatures to low-energy samples to generate smoother distributions and applies lower temperatures to high-energy samples to obtain clearer distributions. In addition, to address the imbalanced dependence of students on prominent features and their neglect of dark knowledge, this study introduces entropy-reweighted knowledge distillation, which utilizes the entropy predicted by teachers to reweight the energy distillation loss on a sample basis. This method can be easily applied to other logic-based knowledge distillation methods and achieve better performance, which can be closer or even better than feature-based methods. This study conducts extensive experiments on image classification datasets (CIFAR-100, ImageNet) to validate the effectiveness of this method.