Facial expression recognition has been increasingly applied in daily life. To address issues such as complex parameters, vulnerability to background interference, and high latency in facial expression recognition models, this study proposes a lightweight expression recognition method based on edge-cloud collaboration and the efficient channel attention (ECA) mechanism. A general model is deployed in the cloud and trained on large-scale datasets. Meanwhile, the shallow convolutional layers of the cloud model are transferred to the edge as feature extractors, which enhance feature extraction, generalization, and reduce the risk of overfitting. On this basis, the ECA mechanism is introduced to enable the model to focus on facial expression feature regions and suppress irrelevant information, further improving recognition accuracy and robustness. Furthermore, using depthwise separable convolution effectively reduces model parameters while maintaining expressive power, significantly lowering computational resource consumption on edge devices. Ultimately, the recognition task is performed at the edge, reducing data transmission overhead and improving response speed. Experimental results show that the accuracy of this method on the CK+ and FER2013 datasets reaches 98.76% and 71.93%, respectively. Compared with traditional methods, while maintaining high accuracy, the number of model parameters is significantly reduced and recognition latency is decreased, verifying the accuracy, efficiency, and deployment advantages of this method for facial expression recognition tasks at the edge.