<p>News recommendation systems are designed to filter extensive information and provide personalized services, central to which is the accurate representation of news and users. This task fundamentally relies on the effective matching of candidate news with user interests. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on the Microsoft News Dataset (MIND) benchmark dataset demonstrated that the proposed model could significantly outperform baseline models. These findings underscore the importance of multi-granularity modeling and provide a more effective solution for capturing complex user interests in real-world recommendation scenarios.</p>