As only one or a few training samples are used for few-shot classification tasks， the features extracted via a prototypical network cannot guarantee much discriminative power. Accordingly， this paper proposes an intra-inter channel attention few-shot classification （ICAFSC） method. This method uses an intra–inter channel attention module （ICAM） to calculate channel weights based on an intra-inter distance metric. The module is integrated into the prototypical network to make the embedded features more discriminative. To overcome the problems of overfitting or underfitting when directly learning the ICAM in the few-shot classification's meta-training stage， ICAFSC adds a pre-training stage before the meta-training of the prototypical network. We design adequate classification tasks with a large number of labeled samples to learn optimal parameters of the ICAM in the pre-training stage. Subsequently， in the meta-training and meta-testing stages of the prototypical network， ICAFSC first freezes the parameters of the ICAM to guarantee a stable channel attention relationship. It then achieves few-shot classification experience learning and transfer via meta-training and meta-testing. We conduct 1-shot and 5-shot few-shot classification experiments on the MiniImagenet dataset. The experimental results indicate that， compared to the prototypical network， the proposed ICAFSC method shows improvements of 1.93% and 1.15% for the 1-shot and 5-shot scenarios， respectively.