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In text streaming data, documents of text stream into system continuously, and huge amount and quick update velocity of data pose great challenges for discovering and tracking topics in such text streaming. In order to handle those prob-lems, this paper proposed a collective matrix factorization based topic discovering and tracking model. In order to deal with mas-sive data, it partitioned the streaming data into batches, and in order to track the evolution of topics, it assumed the current topic distribution as the linear conversion of the previous one. This paper applied collective matrix factorization to represent the cur-rent data as both current topic distribution and previous topic distribution, and got the current topic distribution via factoring both matrices. During the process of solving the optimization problem, it got the parameter update strategy by the Karush-Kuhn-Tucker conditions, and gave the corresponding solving algorithm. During the experimental comparison with Yahoo dataset, the proposed algorithm can better discover topic distribution in text streaming than related works, and also can track the evolution of topics as time going. [ABSTRACT FROM AUTHOR]