[Objective] A neural network model is used to solve the problem of ceramic ware types classification with few samples, and the performance of the model for ceramic ware types classification is improved by using multiscale and attention mechanism optimization.

[Methods] A bottleneck structure based on coordinate attention mechanism and multiscale fusion is proposed and applied to the residual network, which innovatively introduces the relationship between scales and ultimately improves the modeling ability of the residual networks in terms of multiscale.

[Results] On the public dataset of ceramic ware types images, this model achieves a classification accuracy of 95.71% with only a few samples learning, which has an improvement of 1.01% compared with the benchmark model Resnet50. In terms of precision, recall, and F1 score metrics, the present model outperforms the state-of-the-art model Resnest50 by 20.43%, 20.53%, and 20.52%, respectively.

[Limitations] Although the model's recognition accuracy and other metrics have increased, the efficiency of inference has decreased, and it would not be suitable for scenarios where rapid ceramic ware classification is required.

[Conclusions] The multiscale improvement approach is simple and effective in ceramic ware types classification, and this optimization strategy should be prioritized when performing this type of task or similar humanity data.