Special article set is a collection of articles with common background knowledge.In order to more effectively detect the subtopics form special article set with complex information correlation,an LDA subtopic detection algorithm with background noise restraintnamed BLDA is proposed,which improves the precision of subtopic detection from article set by firstly extracting the common background knowledge and then reproducing the keywords in each iteration step.By a series of experiments on a set of WeChat documents from public accounts,it is proved that the detection results obtained by BLDA are much better than those obtained by LDA,with a topic recall rate increment of about 170%,a Purity index increment of 143% and a NMI index increment of 160%.