Cloud detection is an important step in the preprocessing of remote sensing images. The accuracy of cloud detection directly affects the accuracy of subsequent remote sensing image applications. Aiming at the problems of poor generalization ability and serious misdetection and missed detection in the existing cloud and cloud shadow segmentation tasks, this paper designs an improved convolutional neural network model, which uses U-Net as the backbone network and adds efficient channel attention mechanism and modifies activation function. The remote sensing image is used as input and put into the U-shaped cloud image segmentation model based on efficient channel attention for training. After obtaining the optimal weight, the remote sensing image segmentation result including cloud area, cloud shadow area and background area is output. The experimental results show that, compared with the existing segmentation models, this model has lowest parameters, highest accuracy, and best generalization effects in the segmentation task of clouds and cloud shadows.