This paper presents a system to detect burst news on micro-blogging through mining burst words by incorporating features from micro-blog text, propagation periods and other characteristics. To avoid the event mixing problem, this system adopts originality estimation and event demising control to improve the burst news detection based on window-based incremental clustering. The experiments on Sina micro-blogging dataset achieve encouraging performance.