Abstract: In view of the ambiguity of mural images, the differences of mural painting styles in different dynasties, and the high time-consumption and difficulty of the direct traditional manual reference to mural texts or historical documents to identify mural dynasties, an adaptive enhancement capsule network (AEC) algorithm for ancient mural dynasties identification was proposed to automatically identify the dynasties of Mogao Grottoes murals. Based on the original capsule network, the pre-convolution structure was introduced to extract the high-level features of mural images. Secondly, the fitting performance was increased for homogeneous layer activation enhancement model. Finally, the adaptability of the capsule network was enhanced. On the basis of the improved gradient smoothness, the adaptive learning rate was employed to optimize the model, thus improving the classification accuracy of the model. The experimental results show that on the constructed DH1926 mural data set, the accuracy rate of the adaptively enhanced capsule network model is 84.44%, the average accuracy (MAP) is 82.36%, the average recall rate (MAR) is 83.75%, and the comprehensive evaluation index is 83.96%. Compared with other network structures, such as improved convolutional neural network (CNN) and native capsule network, each evaluation index has been improved by more than 3%, and displayed strong fitting performance. It can extract rich features of murals at multiple levels and express more detailed semantic information of images. It is advantageous in higher accuracy and better robustness in the dynasty recognition of Mogao Grottoes murals, and is of certain application value and research significance. 

Key words:  , pre-convolution, homogeneous layer activation, adaptive enhancement, capsule network, mural dynasty classification