A Multi-Level Modeling Framework for Detecting Hidden Risk Patterns in Complex Online Information Environments

In the context of increasingly multi-source and complex online financial information dissemination, traditional single-modality risk detection methods are insufficient for effectively identifying highly concealed and coordinated financial risk behaviors. To address this challenge, a multi-granularity joint financial risk detection framework is proposed, which integrates semantic information content, entity behavioral patterns, and group relational structures. The framework is constructed through an information content semantic risk modeling module, an entity behavior consistency modeling module, and a group coordinated manipulation modeling module, while a hierarchical consistency constraint mechanism is introduced to achieve unified representation and collaborative inference of multi-source risk signals. In this way, the capability of identifying complex financial risks is significantly enhanced. Experimental results on real-world multi-platform financial datasets demonstrate that the proposed method consistently outperforms existing baseline models across all evaluation metrics. Specifically, a Precision of 0.9176, Recall of 0.8943, F1-score of 0.9058, and area under curve (AUC) of 0.9524 are achieved. Compared with the strongest baseline model, FinBERT, improvements of approximately 3.12%, 4.24%, 3.70%, and 3.08% are obtained, respectively. Further multi-granularity comparison experiments indicate that the full-granularity coupling model improves the F1-score by 3.70% over the best single-granularity setting and by approximately 1.5% over the best dual-granularity configuration, thereby validating the effectiveness of multi-granularity information fusion. In addition, ablation studies confirm that each core module and key mechanism contributes significantly to the overall performance. By modeling financial risks from a multi-dimensional sensing perspective, the proposed method not only improves detection accuracy and robustness, but also provides a novel and practical solution for intelligent risk perception and decision-making in complex financial environments.