The speedy and unbounded characteristic of data stream, whose application domain is unceasing increasing, enables mining technology to have a higher challenging more and more. In this paper, we propose a new online mining algorithm called CMNL-SW (Closed Map and Num List-Sliding Window) which uses two data structures, Closed Map stores closed itemsets mined and Num List stores the number of all different items. Via the simple union of item number contained within a new arriving or an old deleting transaction and intersection with certain previous closed itemsets once, it incrementally updates the current sliding window that makes the closed frequent itemsets can be output in real time based on any user’s specified thresholds. Theoretical analysis and the experimental results of the real datasets Mushroom、Retail-chain and artificially synthesized datasets T40I10D100K show that the proposed method is superior to that of state-of-the-art algorithm Moment and CFI-Stream in terms of time and space efficiency, and has good scalability as the number of transactions processed increases and adapts very rapidly to the change in data streams.