Aiming at the problems of low recognition accuracy. poor generalization and detrimental deployment of ground-based clouds, a ground-based cloud map recognition model based on residual network is proposed, named GBcNet. The designed model consists of one convolutional layer, two pooling layers, five residual blocks and one fully connected layer, using the first convolutional layer and the first pooling layer to initially extract feature information and reduce the size of the feature map, extract more feature information through the residual block, while inhibit the overfitting and gradient disappearance of the network, use the other pooling layer to reduce the size of the feature map, and finally output the recognition results of the fully connected layer. The experimental results show that the comprehensive average accuracy of GBcNet model on the dataset reaches 96.02%, and the recognition accuracy of 11 types of ground-based clouds is between 93% and 99%, and has better generalization, and the recognition performance of single category and overall is better than that of other models. Furthermore, the SWIMCAT dataset was used to experiment with the model, and the comprehensive recognition accuracy reached 99.7%, which proved that the model was universally applicable to cloud maps. The model has a simple structure, which is more conducive to marginalized deployment than other models.