Abstract: Topic spotting of segments is performed based on text segmentation and the main topic of the whole text is then generalized. Topics are represented by means of word clusters. LDA (Latent dirichlet allocation) is used to model corpora and text. Clarity is taken as a metric for similarity of blocks and segmentation points are identified by local minimum. The topic words of segments are extracted according to Shannon information. Words which are not distinctly in the analyzed text can be included to express the topics with the help of word clustering of background and topic words association. The signification behind the words are attempted to be digged out. Experiments tell that the result of analyzing is far better than those of other methods. Valuable pre-processing is provided for text reasoning.