In the era of information overload, personalized news recommendation methods are essential to help people find news information that they are interested in. Recognizing user’s multiple interests in news reading is one of the main purposes of news recommender systems. However, most of the existing methods use a unified vector to represent user’s interests without extracting and analysing user’s multiple interests. This leads to incomplete user’s interests modeling and impedes the performance of news recommendation. In this paper, we propose a model named MIMCN (Multi-Interest Modeling with Capsule Network) for news recommendation. MIMCN mainly contains a news encoder and a user encoder for multi-interest recommendation. For news encoder, we introduce a multi-view news encoder that can extract different aspects of features of each news. For user encoder, we introduce a multi-interest modeling method using a customized capsule network to learn user’s multiple interests from their browsing history. We also design a multi-interest aggregation module to avoid noise like misclicks. Experiments on two real-world news recommendation datasets show that our model can achieve better performance than other compared models.