The multi-subtopic is challenging to be understood timely and comprehensively due to micro-blog characteristics, such as low-value density, and fast update speed. For such an issue, this paper proposes a Multi-Subtopic Bi-level Network (MSBN) for micro-blog hot topics based on feature co-occurrence and semantic community division to support users understanding better the subject. First, the highlighted words are extracted by combining two coefficients including the micro-blog importance (e.g., the number of comments and the number of praises) and the time decay. The compound co-occurrence rates (i.e., global and local co-occurrence rates) are used to measure the correlation strength between any two highlighted words, while the global semantic of a micro-blog hot topic can be shown as a complex network whose nodes are the extracted feature words and edges are relations between any two feature words. Next, an improved weighted modularity function is proposed as a criterion for the community division. The complex network of a topic is divided into some semantic communities, where each is regarded as a subtopic of the given micro-blog topic. Subsequently, the genetic algorithm is used to calculate the maximum of weighted modularity and achieve community division of complex networks, so finally, the terminal location of each micro-blog in a different semantic community is obtained to draw regional location map and analyze the supporting propensity of each region to the micro-blog hot topic. Experimental results show that the proposed model can accurately and effectively represent the multi-subtopic of a micro-blog hot topic in the current time that supports users to discover and understand the micro-blog hot topic, allowing users to identify and understand the concerned differences among different regions for the same micro-blog hot topic.

由于微博客特性（例如低值密度和快速更新），难以及时，全面地理解多子主题。针对此类问题，本文提出了一种基于特征共现和语义社区划分的微博客热点话题多主题双层网络（MSBN），以支持用户更好地理解主题。首先，通过组合两个系数（包括微博重要性（例如，评论的数量和赞美的数量））和时间衰减来提取突出显示的单词。复合同现率（即全局和局部同现率）用于衡量任何两个突出显示的单词之间的相关强度，而微博热点话题的整体语义可以表现为一个复杂的网络，其节点是提取的特征词，边是任意两个特征词之间的关系。接下来，提出了一种改进的加权模块化函数作为社区划分的标准。主题的复杂网络分为一些语义社区，每个语义社区都被视为给定微博主题的子主题。随后，利用遗传算法计算加权模块的最大值，实现复杂网络的社区划分，最终获得每个微博客在不同语义社区的终端位置，绘制区域位置图，分析支持倾向。每个区域的微博热点话题。