Abstract: Via analyzing news data on the Internet,an algorithm is presented for news event detection and tracking based on a dynamic evolution model,which borrows the idea of single-pass clustering and combines the specialties of news. The dynamic model is given based on the living characteristics of news event,including similarity computing model based on time distance between news story and news event, event model evolution algorithm,and dynamic threshold idea. This algorithm can automatically organize news data into news special topics,and furthermore provide personalized service for users. Finally,experimental results are used to indicate the validity of the algorithm.