Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent work primarily focuses on using advanced natural language processing (NLP) techniques to extract semantic information from rich textual data, employing content-based methods derived from locally viewed historical clicked news. However, this approach lacks a global perspective, failing to account for users’ hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY(Global-LOcal news Recommendation sYstem), which combines global news representations learned from other users with local news representations to enhance personalized recommendation systems. We accomplish this by constructing a Global Clicked News Encoder, which includes a global news graph and employs gated graph neural networks to fuse news representations, thereby enriching clicked news representations. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and candidate news fusion to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations.