Traditional news recommendation systems regularly update recommendation models, which cannot adjust recommendation lists dynamically according to the change of user preferences. In order to solve this problem, we propose a hybrid recommendation model (IULSACF). It includes two key parts: an item-based incremental update collaborative filtering algorithm and a key word frequency based latent sementic analysis algorithm. The hybrid recommendation model dynamically adjusts the recommendation list by incrementally updating the similarity list of items in the item-based incremental update collaborative filtering module, and combines the latent semantic analysis algorithm to ensure the relevance of recommended articles. Experimental results show that the proposed IULSACF algorithm is superior to traditional recommendation methods in all evaluation indexes.