To address the problem that small-scale receptive fields for wearable activity recognition tasks make it difficult to extract long range associations and that large-scale receptive fields lead to feature compression reducing the network′s resolution for signal features. In this paper, we propose a multi-scale channel attention mechanism based human activity recognition method. Firstly, temporal features and sensor channel features are extracted from multiple receptive fields, so that high semantic features and low semantic features are extracted at the same time to ensure high resolution of features. Secondly, cross channel association is established between multi-scale feature maps to obtain the interaction between low semantic features and high semantic features. multi-scale channel attention mechanism can fully integrate multi-scale features and correlation information of multiple feature maps, enhancing the recognition ability of weak signals and violent signals. The comparative experiments on the UCIHAR, DSADS, PAMAP2 and UniMib-SHAR datasets show that the classification accuracy of our method is improved by 0.43%, 0.75%, 2.90% and 0.83% respectively compared with the state of the art methods.