In few-shot learning scenarios, Model-Agnostic Meta-Learning (MAML) is renowned for its flexibility and applicability independent of specific base model. However, MAML and its variants lack the ability to adaptively adjust task-specific features, such as feature channels, and rely on global initial parameters with fixed inner-loop step numbers, resulting in a training process that lacks interpretability. This paper proposes a rapid, interpretable, and adaptive method using logistic regression and channel attention (ML-LRCA). The goal is to enhance the adaptability and interpretability of the framework by adaptively adjusting task-specific channel weights and utilizing logistic regression to swiftly adapt to task-specific adaptive loss functions. By conducting experiments on multiple open-source datasets for few-shot classification, cross-domain few-shot classification, and few-shot regression, the results indicate that the proposed ML-LRCA method achieves significant performance improvement.