基于VAE-DBN双模型的智能文本分类方法

文本分类技术是信息过滤、搜索引擎等领域的基础，是当下研究热点之一。本文在介绍文本分类相关概念、深度学习相关模型的基础上，通过分析传统文本分类方法存在的不足，提出基于变分自编码器模型和深度置信网络模型（VAE-DBN）的双模型融合的文本分类方法。通过在相关语料集上的对比验证，表明该双模型方法能有效提高文本分类的准确性。

Intelligent Text Classification Method Based on VAE-DBN Dual-Model

Text categorization technology is the foundation of information filtering, search engine and other fields, and is one of current research hot-spots. Based on the introduction of text classification related concepts and deep learning related models, this paper presents a dual-model text classification method based on the variational autoencoder model and the deep belief network model (VAE-DBN) by analyzing the shortcomings of the traditional text classification methods. By comparing and verifying the corpus, the results show that the dual-model method can effectively improve the accuracy of text categorization.