Research on Multimodal Data Fusion Technology based on LLM and Attention Mechanism

This paper studies the practical method of multimodal data fusion technology in traffic. The practical method of multimodal data fusion technology based on LLM and attention mechanism is proposed, and the analysis model of multimodal data fusion technology is constructed, and the quantitative recursive analysis model is used to analyze the constructed data model to realize the feature extraction of constrained feature data. Then, we study the practical method of traffic multi-modal data fusion technology based on LLM and attention mechanism, optimize the number of neurons in the hidden layer neural network, and complete the practical analysis of traffic multi-modal data fusion technology. The simulation experiment results show that the proposed method in this paper can effectively improve the application efficiency and application effect of traffic multi-mode data fusion technology, and provide professional talents for the development of innovative LLM and attention mechanism.