%0 Journal Article %A XIONG Wei %A GONG Yu %T Text Classification Based on Meta Learning for Unbalanced Small Samples %D 2022 %R %J Journal of Chinese Information Processing %P 104-116 %V 36 %N 1 %X To address the semantic and context transfer of text information, an improved method of dynamic convolution neural network based on meta learning and attention mechanism is proposed. Firstly, cross category classification is carried out by using the underlying distribution features of the text to make the text information ready for transfer. Secondly, the attention mechanism is used to improve the traditional convolution network to improve the feature extraction ability of the network, and the balanced variables are generated according to the information of the original data set to reduce the impact of the imbalance of data. Finally, the parameters of the model are optimized automatically by using the two-level optimization method. The experimental results on the general text classification THUCNews dataset show that the proposed method has improved the accuracy by 2.27% and 3.26% in the 1-shot and 5-shot experiments, respectively, and on the IMDb dataset, by 3.28% and 3.01%, respectively. %U http://jcip.cipsc.org.cn/EN/abstract/article_3252.shtml