Aiming at the problems of small quantity， poor quality， difficulty in feature extraction， and similarity of mural text and painting style of ancient mural images， an Inception-v3 model integrated with transfer learning was proposed to identify and classify the dynasties of ancient murals. Firstly， the Inception-v3 model was pre-trained on the ImageNet dataset to obtain the migration model. After fine-tuning the parameters of the migration model on the small mural dataset， the high-level features were extracted from the mural images. Then， the feature representation ability was enhanced by adding two fully connected layers， and the color histogram and Local Binary Pattern （LBP） texture histogram were used to extract the artistic features of murals. Finally， the high-level features were combined with the artistic features， and the Softmax classifier was used to perform the dynasty classification of murals. Experimental results show that， the training process of the proposed model was stable. On the constructed small mural dataset， the proposed model has the final accuracy of 88.70%， the recall of 88.62%， and the F1-score of 88.58%. Each evaluation index above of the proposed model is better than those of the classic network models such as AlexNet and Visual Geometry Group Net （VGGNet）. Compared with LeNet-5， AlexNet-S6 and other improved convolutional neural network models， the proposed model has the accuracy of each dynasty category improved by at least 7 percentage points on average. It can be seen that the proposed model has strong generalization ability， is not prone to overfitting， and can effectively identify the dynasty to which the murals belong.