With the rapid advancement of virtual human technology and live-streaming e-commerce, virtual anchors have increasingly become key interactive entities in the digital economy. However, emerging issues such as fake reviews, abnormal tipping, and illegal transactions pose significant threats to platform financial security and user privacy. To address these challenges, a multimodal emotion–finance fusion security recognition framework (MSF-Net) is proposed, which integrates visual, audio, textual, and financial transaction signals to achieve cross-modal feature alignment and multi-signal risk modeling. The framework consists of three core modules: the multimodal alignment transformer (MAT), the fake review detection (FRD) module, and the multi-signal fusion decision module (MSFDM), enabling deep integration of semantic consistency modeling and emotion–behavior collaborative recognition. Experimental results demonstrate that MSF-Net achieves superior performance in virtual live-streaming financial security detection, reaching a precision of 0.932, a recall of 0.924, an F1-score of 0.928, an accuracy of 0.931, and an area under curve (AUC) of 0.956, while maintaining a real-time inference speed of 60.7 FPS, indicating outstanding precision and responsiveness. The ablation experiments further verify the necessity of each module, as the removal of any component leads to an F1-score decrease exceeding 4%, confirming the structural validity of the model’s hierarchical fusion design. In addition, a lightweight version of MSF-Net was developed through parameter distillation and quantization pruning techniques, achieving real-time deployment on mobile devices with an average latency of only 19.4 milliseconds while maintaining an F1-score of 0.923 and an AUC of 0.947. The results indicate that MSF-Net exhibits both innovation and practicality in multimodal deep fusion and security risk recognition, offering a scalable solution for intelligent risk control in data-driven artificial intelligence applications across financial and virtual interaction domains.

To address the limitations of low fusion efficiency and insufficient personalization in multimodal perception for educational intelligence, a novel deep learning framework is proposed that integrates image sensor data with textual and contextual information through a cross-modal attention mechanism. The architecture employs a cross-modal alignment module to achieve fine-grained semantic correspondence between visual features captured by image sensors and associated textual elements, followed by a personalized feedback generator that incorporates learner background and task context embeddings to produce adaptive educational guidance. A cognitive weakness highlighter is introduced to enhance the discriminability of task-relevant features, enabling explicit localization and interpretation of conceptual gaps. Experiments show the proposed method outperforms conventional fusion and unimodal baselines with 92.37% accuracy, 91.28% recall, and 90.84% precision. Cross-task and noise-robustness tests confirm its stability, while ablation studies highlight the fusion module’s +4.2% accuracy gain and the attention mechanism’s +3.8% recall and +3.5% precision improvements. These results establish the proposed method as a transferable, high-performance solution for next-generation adaptive learning systems, offering precise, explainable, and context-aware feedback grounded in advanced multimodal perception modeling.