Abstract: Weibo is one of the widely used social media platforms for online sharing and communication. Some widely-received topics have been formed into Weibo hot topics by being forwarded, reviewed, and searched by a large number of users in Weibo. And the widespread dissemination of these hot topics may further stimulate and promote users offline behaviors. As a typical representative of it, some hot topics on Weibo may stimulate sales of products related to the topics under the e-commerce platform. Mining out the relevant product categories of Weibos hot topics in advance can help e-commerce platforms and sellers to do a good job of commodity operation and inventory deployment as well as promote the search conversion rate of users and bring about an increase in the sales of corresponding products. This paper proposes a method of mining potential shopping categories associated with hot topics of Weibo. First, the method builds a product knowledge map, and then uses a variety of in-depth network models to perform textual matching between the information of the associated knowledge of product categories and the content of the Weibo topics. The strength of association of each hot topic and product category is identified. Experiments show that the method can effectively identify the relationship between hot topics and shopping categories, and most of the hot topics of Weibo can be associated with at least one product category in the e-commerce platform.