A method for cloud detection of ZY-3 satellite remote sensing images is proposed based on improved deep learning fully convolutional neural network. In pre-trained deep convolutional neural network, full convolution layer is used instead of full connection layer, and deconvolution method is used to up-sample feature map to optimize and improve network structure, then the Adam gradient descent method is adopted to accelerate convergence. The network is trained by using the resource image database of ZY-3 satellite, and the up-sampled image features are input into the Sigmoid classifier . Experimental results show that the proposed method performs better than the traditional methods in terms of detection accuracy and speed. The accuracy achieves 90.11%, and detection time can be reduced to 0.46 s.