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In order to improve the recognition accuracy of cloud-snow satellite imagery in Qinghai-Tibet region, this paper proposes a method combining dense dilated convolutional neural network and dilated convolution to carry out cloud snow satellite image recognition research. The method firstly processes the image by using a conventional convolution layer to obtain a feature map, and then uses a plurality of dense blocks and a transition layer to process the feature map. Among them, the feature transfer of the layers used in the network is realized by using the cross-layer connection in the dense block, so that a large number of cloud snow features are reused, and the gradient disappearance problem during the training process is alleviated. The convolution kernel in the dense block adopts the dilated convolution to expand the local receptive field while reducing the parameter quantity, and extract the feature information of the cloud snow. Finally, the method uses the average global pooling layer and the fully connected layer to obtain the prediction results of the cloud snow image. The experimental results show that compared with other machine learning methods, this method can improve the recognition accuracy of satellite cloud image and has good generalization ability.