Aiming at texture problems, contour similarity among fresco image characters, large differences in fresco character features in different scenes, complex background noise, and confusing classification, an improvement strategy for ResNet convolutional neural network is proposed. Firstly, the larger 7×7 convolutional kernel in the input layer of the model is separated into three series-connected 3×3 small convolutional kernels stacked in the backbone, and 2×2 average pooling and maximum pooling are used for add feature fusion to replace the original maximum pooling operation, which enhances the model′s representative ability. Secondly, a multi-scale efficient spatial channel attention module is designed, based on the ECA channel attention module, the spatial attention module is connected in series, and the original 3×3 convolutional kernel in the spatial module is replaced by the SK attention module, which fuses the multi-scale information to capture the global long-distance dependency, and reduces the interference of background noise. Finally, a cellular aggregation structure is proposed to perform ADD operation on the output information in the neighboring block blocks as inputs to the subsequent layers, capturing both low-level and high-level features to enhance the circulation of contextual information. The experimental results show that the model achieves 96.51%、96.65%、96.67% and 96.63% in accuracy、precision、recall and F1 value, respectively. Relative to the original model ResNet-18 accuracy is improved by 9.76%, and compared with mainstream classification algorithms classification accuracy, generalization ability, and stability are all improved, which can efficiently and accurately identify the type of mural belonging to the mural, which is of significant value for cultural heritage preservation and art history aspects of the research.