Semantic deep understanding model based on multimodal feature fusion and cross-modal alignment

One of the core challenges in natural language processing (NLP) lies in achieving deep semantic understanding, which goes beyond shallow semantic matching to capture the complex emotions, intentions, and contextual dependencies embedded in multimodal data. This paper proposes a novel deep semantic understanding model that introduces a hierarchical multimodal fusion mechanism and a self-supervised cross-modal alignment paradigm. First, dedicated encoders extract fine-grained features from text, vision, and audio modalities. Then, an attention-driven hierarchical fusion network dynamically integrates and filters salient information at the feature level, semantic level, and decision level, ensuring efficient cooperation across modalities. Simultaneously, this paper designs a contrastive learning-based alignment module that narrows the representational gap between semantically related multimodal pairs while pushing apart irrelevant ones in a shared semantic space, thereby implicitly learning deep semantic consistency and alleviating the “modality gap” problem. The proposed method is systematically validated on multiple public benchmark datasets as well as domain-specific multimodal datasets. Experimental results demonstrate that our approach consistently outperforms strong baselines, including CNN, BiLSTM, Transformer, and Multimodal-BERT. Specifically, the proposed method achieves 94.7% accuracy and an F1-score of 94.2% for multimodal sentiment recognition, surpassing Multimodal-BERT by approximately 3.4% in F1-score. In cross-dataset transfer tasks, the model maintains strong generalization, achieving 90.5% F1-score when transferring from Dataset-A to Dataset-B, and exhibits faster convergence with lower variance in validation accuracy. These findings not only provides a powerful solution for multimodal semantic understanding but also offers a new technical path for exploring the development of cognitive intelligence in machines.