In order to better realize the automatic classification and change detection of remote sensing images, this paper proposes an automatic remote sensing image classification model based on CNN and migration learning, and constructs a remote sensing image change detection model by combining CNN and Transformer network. In the remote sensing image classification model, DenseNet network and Inception network are used as the backbone network, combined with the new channel attention module to mine the image features of remote sensing images, and then realize the accurate classification of remote sensing images. In the remote sensing image change detection model, the convolution operation of CNN with different sizes of void rate and expansion rate is utilized to better guide the feature map to focus on local information. Combined with the dynamic deformable Transformer to provide more accurate remote sensing image location information and detail information, to reduce the impact of background interference on remote sensing image change detection, and to improve the model’s ability to recognize the target of remote sensing images. The parameter count and floating-point computation of the remote sensing image classification model are 7.69MB and 1.89GB, respectively, which are smaller than the parameter count and floating-point computation of the single network model. The RSICD models mF1 and mIoU are 1.66% and 0.58% higher than the optimal ones. Through the effective integration of convolutional neural networks and many different types of deep learning techniques, automated classification and change detection of remote sensing images can be realized.