In the field of facial expression recognition, low parameters and high accuracy are difficult to complete together, A facial expression recognition method based on ShuffleNetV2 network combined with attention mechanism is proposed in this study. Based on ShuffleNetV2 architecture, this method further improves the feature capture and classification capability of the model by fine-tuning the model and replacing the Relu activation function with the PRelu activation function. In addition, this paper introduces an ultra-lightweight dual attention module LDAM, which combines DCAM attention mechanism and spatial attention mechanism, and integrates it into the optimized ShuffleNetV2 model by shortcut connection technology, so as to enhance the model′s recognition ability and classification effect of detailed features. Experimental results on FER2013 and CK+, two widely recognized facial expression recognition datasets, show that the proposed method achieves recognition accuracy of 69.12% and 94.77%, respectively, while maintaining a model parameter count as low as 1.25. This result is not only demonstrated in the possibility of maintaining the lightweight model while improving the recognition performance, but also verifies the efficiency and practicability of the proposed method through experiments.