The widespread dissemination of false information can lead to serious harm, such as misleading public perception and severely undermining the credibility of mainstream media. Considering that traditional multimodal false information detection methods struggle to handle high-risk trending information and highly consistent text-image false information, this paper establishes a multimodal detection model based on a news corpus for verifying trending information, enabling early detection of text-image false information. Compared with traditional false information detection methods, this model introduces the concepts of a hot news time window and a high similarity spatial window, analyzing the authenticity of information from both popularity and novelty perspectives. It designs a data preprocessing pipeline including OCR text recognition and completion, image masking, etc., to improve dataset quality, and integrates text-image features to overcome the limitations of single-modal approaches. Experiments on the constructed dataset demonstrate that the proposed model outperforms existing baseline models in terms of performance metrics such as accuracy and precision.