In order to solve the problems of embarrassing sales under the impact of e-commerce and time consuming and exhausting effort of users when chosing the commodity under the "information explosion" of the Internet, this paper introduces the location model of the business-circle based on the circular filtering method and the improved partition-based DBSCAN density clustering algorithm for Zhejiang Province. The geographic location characteristics of the 250 000 merchants' order data in a certain industry of Zhejiang Province are analyzed, and the traditional recommendation algorithm is improved by combining the time decay parameters. The commodity recommendation algorithm for the popularity of the business-circle and the collaborative filtering algorithm for the similarity of the business-circle are proposed. The experimental results show that the algorithm is superior to the traditional recommendation algorithm in terms of recommendation accuracy rate, and to some extent, it alleviates the problem of insufficient cold start and recommended product surprise, which has its practical value and research significance.