Hot trends are likely to bring new business opportunities.For example, "Air Pollution" might lead to a significant increase of the sales of related products, e.g., mouth mask.For ecommerce companies, it is very important to make rapid and correct response to these hot trends in order to improve product sales.In this paper, we take the initiative to study the task of how to identify trend related products.The major novelty of our work is that we automatically learn commercial intents revealed from microblogs.We carefully construct a data collection for this task and present quite a few insightful findings.In order to solve this problem, we further propose a graph based method, which jointly models relevance and associativity.We perform extensive experiments and the results showed that our methods are very effective.