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Web recommendation system is an important research content of web mining. In this paper, we propose a new web recommendation system model based on cluster mode to realize the real-time online recommendation. First, we use a new method to get the feature vector based on tf-idf method. Second, we use an unsupervised web page clustering algorithm to realize user clustering. According to the result of clustering, we use naïve Bayesian method to predict user's action according to its web navigation. Experimental evidence shows that using this method to explain users' active browsing goals is effectively enhanced.