Mining the hierarchical structure of Internet review topics and realizing a fine classification of review texts can help alleviate users’ information overload. However, existing hierarchical topic classification methods primarily rely on external corpora and human intervention. This study proposes a Modified Continuous Renormalization (MCR) procedure that acts on the keyword co-occurrence network with fractal characteristics to achieve the topic hierarchy mining. First, the fractal characteristics in the keyword co-occurrence network of Internet review text are identified using a box-covering algorithm for the first time. Then, the MCR algorithm established on the edge adjacency entropy and the box distance is proposed to obtain the topic hierarchy in the keyword co-occurrence network. Verification data from the Dangdang.com book reviews shows that the MCR constructs topic hierarchies with greater coherence and independence than the HLDA and the Louvain algorithms. Finally, reliable review text classification is achieved using the MCR extended bottom-level topic categories. The accuracy rate ($P)$, recall rate ($R)$ and $F$1 value of Internet review text classification obtained from the MCR-based topic hierarchy are significantly improved compared to four target text classification algorithms.

挖掘互联网评论主题的层次结构，实现评论文本的精细分类，有助于缓解用户信息过载。然而，现有的分层主题分类方法主要依赖于外部语料库和人工干预。本研究提出了一种改进的连续重整化（MCR）程序，作用于具有分形特征的关键字共现网络，以实现主题层次挖掘。首先，首次使用框覆盖算法识别互联网评论文本的关键词共现网络中的分形特征。然后，提出了基于边缘邻接熵和框距离建立的MCR算法，以获得关键字共现网络中的主题层次结构。当当网的验证数据。com 书评表明，与 HLDA 和 Louvain 算法相比，MCR 构建的主题层次结构具有更高的连贯性和独立性。最后，使用 MCR 扩展的底层主题类别实现了可靠的评论文本分类。准确率（$磷)$, 召回率 ($R)$和$F$与四种目标文本分类算法相比，从基于 MCR 的主题层次结构获得的 Internet 评论文本分类的 1 值显着提高。