针对目前基于形式概念分析的文本处理主要使用关键词作为形式背景属性，属性之间存在关联、重叠、重复且属性内容不区分主题，导致无法满足特定目标或主题的文本挖掘需求，以及规模化文本概念格构建时间长、单格耗费空间多等问题，本课题研究引入LDA主题模型构建形式背景，设计一个基于主题形式概念分析的文本处理模型。首先，利用主题比关键词信息粒度大且更能表达文本语义信息的优点，使用概率主题模型计算并构成文本数据的形式背景，设计形式概念在概念格上下文中的主题联合分布模型，表达形式概念的典型性或重要性；其次，设计一个分布式概念格构建模型，按主题对形式背景进行分解，对分解背景构成的概念格进行多格同时合并，达到多主题概念融合，并能够提升概念格构建效率；最后，结合学术论文检索，使用主题形式概念分析模型，提出一种基于查询结果集的多文档摘要构建框架，以满足特定主题要求的摘要内容表达及覆盖。

Since Formal Concept Analysis-based text processing always uses keywords as the attributes of formal context, and there exist the correlation, overlap, and repetition between keywords, and the attributes usually remain with mixtures of topics which will lead to the result of text mining does not meet the special information requirements from users. Furthermore, concept lattice’s building on large scale text dataset is time and space consuming. According to the above situations, we combine LDA probabilistic topic model into Formal Concept Analysis. First, we define a textual topical Formal Concept Analysis model in which the attributes can be calculated by probabilistic topic model which can express the attributes’ semantic metric own to some topic or topics in real text mining. And the formal concepts in the lattice built by topics have its joint probabilistic of topic(s) with which we can develop a method to evaluate the importance or typicality of the formal concepts. Second, a distributed processing model is designed, including the decomposition of formal context by topic and the union of several sub concept lattices, which can improve the efficiency of lattice’s construction. At last, with the query result from scholar search engine, we propose a multi-document abstract construction framework based on the proposed model which can fulfill the request of presenting a text summarization with special topic purpose and enough content covering.