Objective Traditional convolutional neural networks face challenges in face expression recognition due to complex network structures and weakened feature extraction particularly from critical regions like eyes and lips that reflect emotional states. This leads to issues such as weak specificity and neglect of spatial structural information in facial expressions resulting in lower recognition accuracy. In response this study proposes a face expression recognition method embedded with attention mechanisms and channel shuffle based on mainstream approaches in the field. Methods Firstly preprocessed facial images are fed into a spatial attention module to enhance spatial dimensional information allowing the model to better focus on key areas within the images. Secondly the channels of the obtained spatial attention feature map are split to make the information flow pass through different paths and then channel fusion is carried out to enhance the ability of feature expression. Subsequently the obtained feature maps are introduced into the channel attention module to enhance the channel dimension information. Finally global average pooling is applied for network prediction. Results The designed network achieves recognition accuracies of 71. 80% on the FER2013 dataset and 99. 66% on CK+ with only 1. 9 M parameters. Conclusion This method outperforms many traditional classic algorithms providing a more effective approach to improving face expression recognition accuracy. This method holds significant practical value and promising application prospects.