Unlike previous works such as explicit consumption intent recognition research, this paper presents a method that uses user behavior analysis to automatically recognize the implicit consumption intent. Specifically, the proposed method recasts implicit consumption intent recognition as a multi-label classification problem, which combines multiple features based on follower's behavior, intent behavior, retweets behavior, and user profiles. The paper proposes a method for the automatic extraction of a large user linkage across social media. With the proposed method, more 120000 user linkage pairs are extracted. Experimental results show that the multi-label classification-based method is effective for implicit intent recognition. Especially, the exploited features are all helpful for improving the recognition performance.