Abstract: A lightweight micro convolutional neural network（M-CNN） model with high recognition rate is proposed for EL images of solar cells. The network model incorporates a fusion channel attention mechanism to merge multiple feature maps. Introducing Ghost convolutional layers to reduce the model parameters, and using ordinary convolutional layers to replace maximum pooling layers for feature space dimensionality reduction. Experimental results show that on a self-built database of 15767 EL images of cracks, shadows, minor defects, and no defects, M-CNN achieves an accuracy of 99.83% and 93.38% for rough classification and flaw classification detection, respectively, with a model parameter count of 1.29 MB. Notably, compared to advanced networks such as MobileNetV3, DeepVit, and MobileVit, M-CNN has the advantages of superior defect recognition rate and lower model parameter count.