Multimodal Learning Data Fusion and Analysis Based on Self-Attention Mechanism

Multimodal data fusion is one of the research hotspots in the field of artificial intelligence. Traditional multimodal learning methods face numerous challenges in data heterogeneity, information complementarity, and cross-modal alignment. This paper proposes a multimodal learning data fusion method based on the self-attention mechanism (MLDFA), which fully explores and utilizes the correlation information between different modalities to improve the effectiveness and robustness of data fusion. Firstly, this paper introduces a cross-modal adaptive attention mechanism, which dynamically assigns weights to different modal data by incorporating an improved self-attention mechanism, adaptively adjusting information flow to enhance the expressive power of key features. Secondly, this paper employs a hierarchical feature alignment strategy, constructing multi-level semantic alignment modules combined with global-local feature mapping to make information interaction between modalities more precise. Finally, we propose a multi-view fusion framework, leveraging the combination of multi-head attention mechanisms and graph neural networks (GNN) to capture higher-order correlations of different modal features, achieving more efficient fusion. The experimental section validates the proposed method on multiple public datasets, and the results show that our method outperforms existing mainstream methods, demonstrating its effectiveness and scalability in multimodal learning tasks.