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In order to overcome the shortcoming of the static threshold and the problem of the false reports, this paper put forward double filtering based on dynamic threshold and named entity of topic tracking. This method used the KL divergence to obtain the initial threshold and the candidate reports. Then, according to the time characteristics of reports s it studied dynamic threshold method. Finally, it extracted named entities of the reports to calculate similarity of named entities and the same number of named entities, which completed topic tracking. The experiment proves the feasibility of the algorithm and improves the efficiency of the topic tracking to a certain extent. In addition, it reduces the rate of false positives and non-response rates. [ABSTRACT FROM AUTHOR]