In recent years, integrating text and image data for sentiment analysis in social networks has become a key approach. However, techniques for capturing complex cross-modal information and effectively fusing multimodal features still have shortcomings. We design a multimodal sentiment analysis model called the Dynamic Graph-Text Fusion Network (DGFN) to address these challenges. Text features are captured by leveraging the neighborhood information aggregation properties of Graph Convolutional Networks, treating words as nodes and integrating their features through their adjacency relationships. Additionally, the multi-head attention mechanism is utilized to extract rich semantic information from different subspaces simultaneously. For image feature extraction, a convolutional attention module is employed. Subsequently, an attention-based fusion module integrates the text and image features. Experimental results on the two datasets show significant improvements in sentiment classification accuracy and F1 scores, validating the effectiveness of the proposed DGFN model.