Research on Multimodal Data Fusion and Analysis Methods Driven by Knowledge Graph

In this paper, a new Depth Mixed-RESnet (DM-RN) model is proposed through multi-mode fusion, which aims to organically integrate the logic of depth-first search (DFS) algorithm, the residual learning advantage of ResNet and the Mixed attention (MA) mechanism, so as to promote the research of multi-modal data fusion and analysis methods driven by knowledge graph. The DM-RN model optimizes the depth traversal of the network structure by DFS strategy to ensure the effective transmission of information. Combined with ResNet residual connection, the problem of gradient disappearance in deep network training can be effectively alleviated and the representation ability of the model can be improved. Mixed attention mechanism is introduced to enhance the ability of the model to capture key information in multi-modal data. Finally, by comparing with traditional multi-module data, the experimental results show that DM-RN method has significant advantages in improving the accuracy of multi-mode data fusion and analysis and accelerating the loss function. This method can not only integrate heterogeneous data from multiple sources efficiently, but also improve the analysis accuracy and generalization performance.