[Objective] This paper aims to propose an algorithm to build “Feature Items Ontology”. [Context] Trending topics online are constantly changing and involve extensive fields. The existing research on automatically creating Ontology is limited to specific areas, which cannot effectively process the dynamic trending topics. [Methods] First, we analyzed the contents of major events from the trending topics. Second, we designed an algorithm automatically generating the Ontology. Third, with the guidance of initial Ontology, proposed an evolutionary algorithm to track the changing topics. [Results] Using the case of “Wei Zexi and Baidu” as an example, we collected 11,174 Sina Weibo posts to conduct two rounds of experiment. We initially extracted 7,421 feature items, 39 key nodes, and 781 key relationships. For the evolutionary results, we got 24,564 feature items, 67 key nodes, and 1,818 key relations. The missing rates, the false positive rates, and the loss costs were 0.1261, 0.0964 and 0.5985, which were all better than those of the TF-IDF algorithm. [Conclusions] The “Feature Items Ontology” is more accurate than the single word Ontology description, and is easier to calculate the semantic similarity. It is an appropriate method to retrieve semantic information from the dynamic trending topics.
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