Graph Neural Network Model Under Multi-Modal Fusion

In the real world, data usually exhibits multimodal characteristics with different sources and formats. Traditional unimodal processing methods often fail to fully utilize the correlation and complementary information between different modalities. In this paper, we integrate various modal data into a graph neural network framework to effectively learn and analyze the complex relationships between nodes and edges. Through the techniques of feature fusion, decision fusion and deep fusion, multimodal data can be comprehensively analyzed to extract more meaningful insights. The experimental results show the effectiveness of the method, and the classification accuracy is significantly improved, reaching up to 98 %. The recall rate reaches 86.9% and the F1 score is 0.964. These metrics highlight the strong performance that the model shows in complex, realistic scenarios dealing with multimodal data. The model shows significant potential in handling multimodal data, providing a robust and efficient method for complex data analysis. This demonstrates that in the real world, utilizing the rich information of multimodal data can lead to deeper insights and superior performance.