With the rapid advancement of social media platforms like Weibo and WeChat, alongside the emergence of deepfake technologies, tackling fake information has become a major challenge for society and government institutions. To address this, developing efficient and intelligent methods for fake news detection is crucial. This paper introduces a dual-stream fusion network model (DSF-MHSA) based on deep learning, designed to detect fake news across web pages, images, and text. The model tackles issues such as cross-lingual discrepancies, data imbalance, and multimodal information fusion by integrating deep learning models like ERNIE-M, AlexNet, and ShuffleNet, along with three multi-head self-attention mechanisms. It processes textual and image data separately to capture long-range dependencies and global information, enhancing understanding and recognition. A unified multi-head self-attention mechanism then merges these insights to strengthen cross-modal correlation detection. The model is tested on datasets from Twitter, Weibo, and IKCEST-2023, which includes real online news from various social media sources. Results show that the DSF-MHSA model achieves over 90 % accuracy, surpassing traditional models in news detection tasks. This research offers significant practical value for the identification and understanding of news content.