[目的/意义] 基于网络社交平台中社群话题及用户兴趣挖掘而生成的社群标签，能够提高社群定义的及时性与准确性，解决用户信息获取、网络社群选择的困难。[方法/过程] 通过对网络社群的深入分析，发现社群特征可根据社群话题及用户兴趣予以表征。首先，利用主题提取BTM模型对网络社群话题进行主题模型训练，从而得到网络社群话题预标签；其次，根据社群成员兴趣标签网络中不同类型的重要节点指标，利用TOPSIS多指标综合评价方法挖掘成员整体兴趣，从而得到网络社群成员兴趣预标签。综合两者结果生成社群标签并进行优化，且以"豆瓣小组"为例进行实证。[结果/结论] 基于社群话题及成员兴趣的社群标签生成模型能够准确地挖掘主要兴趣及近期关注点，社群整体的标签生成有利于网络用户兴趣群体的选择。
[Purpose/significance] Community tags generated based on the mining of community topics and users' interests in network social platforms can improve the timeliness and accuracy of the definition of community, and solve the difficulties of user information acquisition and network community selection. [Method/process] Through in-depth analysis of the network community, it was determined that the community features can be represented according to the community topics and users' interests. Firstly, the BTM model of topic extraction was used to train the topic model of network social topics, and the pre-label of network social topics was obtained. Then, based on the different important node indexes of community members' interest tag network, the TOPSIS multi-index comprehensive evaluation method was used to mine the overall interest of members, so as to obtain the interest pre-label of members of the network community. After combining the two results, the community tag was generated and optimized. And this paper took "Douban Group" as an example for demonstration. [Result/conclusion] The community tag generation model based on community topics and members' interests can accurately mine the main interests and recent concerns. Tag generation of the community as a whole is conducive to the selection of interest groups of network users.
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