Discovering the hot topic among trends is an essential way to manage public opinion. But the dynamic property makes it a tough task as the existing methods of detection process are time-consuming. With the new Fast discovery of burst spatial-temporal topic (FDBST) method, the uptime of topic discovery is kept within one second and there is no sacrificing of topic quality, the noisy topics are all kept off, the time-varying problem and the sparsity problem are easily tackled by using spatial-temporal characteristics. How the FDBST works? It is triggered by a burst term based on social data trends, while the data is generated within a small time interval in a region while the irrelevant data is excluded during this process. By fusing the regional topics, the potential burst topic is obtained.The experiments show the preferable effects of the FDBST and it is an outperforms state-of-the-art approaches in terms of effectiveness.