With the rapid development of artificial intelligence technology, text categorization technology is becoming more and more mature. However, text categorization in real situations still faces various unconstrained conditions. English text is an important part of text information, it is also an important way for people to get information from abroad. How can everyone get the desired content from the massive data quickly and accurately, it has become a hot issue in current research. This paper improves the current text categorization algorithm based on English quality-related text categorization. The design and implementation of text categorization system are illustrated with an example of English quality-related text categorization system, complete the research work of text categorization algorithm. The core work of this paper is to mine, classify and analyze large amounts of data in English text by using the method of combining cyclic neural network with quality. Finally, the essential features of high quality English texts are obtained. Traditional English text categorization algorithm if the amount of training data is large, it is easy to show some defects such as unclear feature items. In view of these problems, in order to improve the accuracy and flexibility of English text categorization, this paper proposes a quality-related English text categorization method based on cyclic neural network. A mechanism combining attention is proposed to improve the problem of label disorder and make the structure of the model more flexible. The model proposed in this paper is compared and optimized. Experiments show that the accuracy of neural text classification based on quality classification can reach about 96%.

随着人工智能技术的飞速发展，文本分类技术变得越来越成熟。但是，实际情况下的文本分类仍然面临各种不受限制的条件。英文文本是文本信息的重要组成部分，也是人们从国外获取信息的重要途径。每个人如何快速，准确地从海量数据中获得所需的内容，这已成为当前研究的热点。本文改进了基于英文质量相关文本分类的当前文本分类算法。以英语质量相关的文本分类系统为例，说明了文本分类系统的设计与实现，完成了文本分类算法的研究工作。本文的核心工作是挖掘，采用循环神经网络与质量相结合的方法对英文文本中的大量数据进行分类和分析。最后，获得了高质量英语文本的基本特征。传统的英语文本分类算法如果训练数据量大，就容易表现出一些缺陷，例如特征项不明确。针对这些问题，为提高英语文本分类的准确性和灵活性，提出了一种基于循环神经网络的质量相关的英语文本分类方法。提出了一种结合注意力的机制，以改善标签混乱的问题，并使模型的结构更灵活。对本文提出的模型进行了比较和优化。