With the increased popularity of mobile devices, local search has become a new popular service. Therefore, we need a powerful POI (Points of Interest) database to support local search. In recent years, the web has become the largest data source of POIs. With the prevalence of Internet, people will share their travel experience and information of POIs that they had been visited on social network, their blogs, and even check-in post. Besides, many companies and organizations publish their business on their own websites, resulting a large number of POIs. In this paper, we propose a POI database construction system from the immense data of the Web. Our system consists of two parts: the query-based crawler, and the POI extraction system. The goal of query-based crawler is to collect address-bearing pages (ABP) from the web as address is a good indicator of POIs. The second part is POI extraction system. We use CRF (Conditional Random Field) to train a Chinese postal address recognition model and a Chinese organization recognition model. After the extraction of addresses and POI names from ABP with these two CRF models, we then leant a model to pair an address and a POI name as a POI. Finally, we extract POI associated information for each POI to construct a complete POI data.