The information overload problem becomes imperative with the explosion of information, and people need some agents to facilitate them to filter the information to meet their personal need. In this work, we conduct a research for the article classification in the tourism domain so as to identify articles that meet users¡¦ information need. We propose an information need orientation model in tourism, which consists of four goals: Initiation, Attraction, Accommodation, and Route planning. These goals can be characterized by 13 features. Some of the identified features can be enhanced by WordNet and Named Entity Recognition techniques as supplement techniques. To test the effectiveness of using the 13 features for classification and the relevant methods, we collected 15,797 articles from TripAdvisor.com, the world's largest travel site, and randomly selected 600 articles as training data labeled by two labelers. The experimental results show that our approach generally has comparable or better performance than that of using purely lexical features, namely TF-IDF, for classification, with fewer features.