Research on Multi-Source Data Fusion Analysis Model Based on Deep Learning

A multi-source data fusion model is presented, which is based on the deep learning. With the increasing demand for multi-source data fusion in fields such as the Internet of Things, intelligent transportation, and medical health, how to efficiently integrate data from different modalities (such as images, texts, sensor data, etc.) has become a research hotspot. Traditional methods have limitations in processing large-scale and heterogeneous data, while deep learning technology provides a new solution for multi-source data fusion with its powerful feature extraction and pattern recognition capabilities. The core of this paper is the original deep multimodal fusion network (DMFN). By introducing a dynamic feature alignment method based on the attention mechanism, the network can automatically learn the optimal alignment between different modalities, thereby improving the fusion accuracy and robustness. Compared with traditional methods, the DMFN model shows higher flexibility and adaptability in the process of feature extraction and fusion. The DMFN model is validated on several standard data sets, including Smart Transport Data Set and Medical Image and Text Dataset. The results indicate that the DMFN model has an average precision of 15.3%, a 22.7% increase in robustness and a 38.5% reduction in reasoning time. Moreover, the dynamic learning capability of the model is proved by visual analysis, which further proves the validity of the model.