The traditional cloud detection methods are less effective in recognizing special scenes, which cause problems such as edge information loss and thin and broken clouds misjudgment. In this study, MSHA-DeepLab algorithm based on multi-scale feature fusion and hybrid attention is proposed for high-precision cloud detection. First, the attention module is introduced based on the original algorithm, which to increase the weight of important features and improve the sensibility of local features. Second, depthwise separable convolutions are used to extract the multiscale semantic information and reduce the amount of network parameters. Finally, continuous up-sampling and feature fusion are performed to reduce the loss of feature information. After testing and comparing the datasets with different scenes and different band combinations using various methods and the improved algorithm, it can be seen that the precision of the algorithm reaches 86.376 9%, the recall reaches 85.895 9%, the sepecificity reaches 96.915 6%, the IoU reaches 82.846 7%, the accuracy reaches 94.600 8%， which is a significant improvement compared with the original algorithm and other methods. It is verified that the proposed algorithm can achieve high accuracy cloud detection under different conditions.