<p>Effective news recommendation is crucial for alleviating users’ information overload. While recent prompt-based news recommendation methods have shown promising performance by reformulating the recommendation task as a masked prediction problem, we note that this paradigm still faces several major limitations including inadequate multi-interest representation, limited global interaction modeling, and historical interaction truncation. To address these problems, this paper proposes PCRec, a prompt-guided cross-view contrastive learning framework for multi-interest news recommendation. PCRec first introduces feature-level prompts to overcome the input constraints inherent in text-level prompts. Moreover, a two-stage user modeling module is designed to capture users’ multi-interests. Finally, to model global usernews relationships, PCRec implements a cross-view contrastive learning strategy. This approach groups similar users, enabling learning from multiple perspectives and breaking down isolated relationships among users, news categories, and news subcategories. Extensive experiments on two real-world news recommendation datasets validate the superiority of our proposed PCRec compared with various state-of-the-art baselines.</p>