Neural networks have been widely used in the field of text classification, and have achieved good results on various Chinese datasets. However, for long text classification, there are a lot of redundant information in text data, and some of the redundant information may involve other topic information, which makes long text classification challenging. To solve the above problems, this paper proposes a new text classification model, called attention-based BiLSTM fused CNN with gating mechanism(ABLG-CNN). In ABLG-CNN, word2vec is used to train word vector representation. The attention mechanism is used to calculate context vector of words to derive keyword information. Bidirectional long short-term memory network(BiLSTM) captures context features. Based on this, convolutional neural network(CNN) captures topic salient features. In view of the possible existence of sentences involving other topic information in long texts, a gating mechanism is introduced to assign weights to BiLSTM and CNN output features to obtain text fusion features that are favorable for classification. ABLG-CNN can capture text context semantics and local phrase features, and perform experimental verification on two long text news datasets. The experimental results show that ABLG-CNN’s classification performance is better than other latest text classification methods.

神经网络已广泛用于文本分类领域，并在各种中文数据集上取得了良好的效果。然而，对于长文本分类，文本数据中有很多冗余信息，并且某些冗余信息可能涉及其他主题信息，这使得长文本分类具有挑战性。为了解决上述问题，本文提出了一种新的文本分类模型，即具有选通机制的基于注意力的BiLSTM融合CNN（ABLG-CNN）。在ABLG-CNN中，word2vec用于训练单词向量表示。注意机制用于计算单词的上下文向量以导出关键字信息。双向长期短期存储网络（BiLSTM）捕获上下文特征。在此基础上，卷积神经网络（CNN）捕获了主题的显着特征。考虑到长文本中可能存在涉及其他主题信息的句子，引入了门控机制，为BiLSTM和CNN输出特征分配权重，以获得有利于分类的文本融合特征。ABLG-CNN可以捕获文本上下文语义和本地短语特征，并对两个长文本新闻数据集进行实验验证。实验结果表明，ABLG-CNN的分类性能优于其他最新的文本分类方法。并对两个长文本新闻数据集进行实验验证。实验结果表明，ABLG-CNN的分类性能优于其他最新的文本分类方法。并对两个长文本新闻数据集进行实验验证。实验结果表明，ABLG-CNN的分类性能优于其他最新的文本分类方法。