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A text mining model for topical evolutionary analysis was proposed through a text latent semantic analysis process on textual data. Analyzing topic evolution through tracking the topic different trends over time. Using the LDA model for the corpus and text to get the topics, and then using Clarity algorithm to measure the similarity of topics in order to identify topic mutation and discover the topic hidden in the text. Experiments show that the proposed model can discover meaningful topical evolution.