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Matrix factorization algorithm has been widely used in recommender system because of its high predic-tion accuracy and good scalability. However, most of the current matrix factorization algorithms deal with static data. With the gradual increase of training data, traditional matrix factorization method needs to retrain all existing data to update the model, which brings about increase of time cost and calculation cost. Therefore, how to score and predict items in a short time to make reasonable and accurate recommendation is the main problem of research. In order to solve this problem, an improved incremental matrix factorization algorithm is proposed. The main idea is to process the data in different regions according to score source in prediction process. The method can effectively shorten the calculation time and keep the accuracy within a certain range. In the static training module, the initial user and item feature training will not occupy the online training time, and can obtain better accuracy when the initial data are large; in the dynamic training module, the corresponding small dynamic matrix is extracted and trained from the corresponding scores of new user set and item set, and then the small dynamic matrix is dynamically maintained and updated. Subsequent feature training is performed on this small matrix. At the same time, in order to ensure the training accuracy and reduce the training time of dynamic matrix, a fast update strategy based on random gradient descent method is adopted. This method effectively shortens the time and improves part of the accuracy. Experimental results on two open datasets show the superiority of the proposed algorithm.