关键词: 深度学习, 连续词袋模型（CBOW）, 注意力机制, 神经网络, 文本分类

Abstract: The representation and the feature extraction of text are the core problems that need to be solved in text classification. Based on this, a text classification model based on improved Continuous Bag-of-Words（CBOW） and ABiGRU is proposed. The classification model uses the word vector trained by the improved CBOW model as a word embedding layer, and then the features of the text are fully extracted through the convolutional and pooling layers of the convolutional neural network and the bidirectional gated recurrent unit neural network combined with the attention mechanism. The text feature vector is input to the softmax classifier for classification. In this paper, text categorization experiments are carried out in three datasets, the experimental results show that the proposed method has better performance than other text categorization algorithms.

Key words: deep learning, Continuous Bag-of-Word（CBOW）, attention mechanism, neural network, text classification