Brain tumors are one of the most common diseases in the nervous system， and magnetic resonance imaging （MRI） examination is a common screening method for brain tumors. Although deep learning models have achieved high performance in MRI image recognition， their operational effectiveness is unsatisfactory in scenarios with limited computational resources， and their performance tends to decline after pruning for lightweight. Therefore， this study focuses on improving the brain tumor MRI image classification performance of lightweight models. To address the aforementioned challenges， classification method of variable-temperature self-distillation brain tumor MRI images based on label-auto-calibration is proposed. Starting from the core requirement of enhancing the performance of lightweight models， this study employs a loss function based on label-auto-calibration loss to avoid imparting incorrect knowledge from the self-teacher model during the self-distillation process. Additionally， a temperature-resetting variable-temperature distillation mechanism is introduced. When the validation accuracy does not improve in recent rounds of training， the temperature is reset based on the current training situation to prevent the lightweight network from encountering excessive learning difficulty. Experimental analysis on multiple brain tumor datasets demonstrates that the proposed self-distillation method can effectively enhance the brain tumor recognition performance of lightweight models， and its performance surpasses other typical self-distillation methods. This method requires no changes to the model structure， is easy to implement， and can be used to enhance the performance of lightweight networks.