Aiming at the complicated problem of cloud image feature extraction method of all-sky imager, we propose a cloud-type classification model, that is, a dual-path gradient convolutional neural network (DGNet), by combining a double-line dense structure and gradient information to optimize the ability of the network to learn features of cloud images. The classification model is constructed using dual-thread parallel dense modules, and a gradient algorithm is applied to the feature maps. Experimental results show that compared with classic models, the accuracy of the proposed model improves significantly, reaching 67.00%. The main contributions of this study are as follows: the proposed model adopts a multithread and multiscale gradient dense module structure to reduce the loss of feature information; The gradient algorithm is used to fully extract the gradient change features of the cloud image to enhance the model’s accuracy for recognizing cloud species; A new data set of all-sky images is proposed, which contains 10 types of cloud images and 100 images of each type, accounting for 1000 images; Compared with the existing models, the proposed model shows the best accuracy, proving the feasibility of the proposed model.