Research on multimodal big data analysis and mining algorithm optimization based on deep learning

Aiming at the key problems in multimodal big data analysis and mining, this paper proposes an algorithm optimization framework based on deep learning. The algorithm framework designed in this paper realizes efficient processing of multimodal data through deep learning technology, and its core processes include input layer, feature extraction layer, feature fusion layer, task layer and optimization layer. In the stage of feature extraction and fusion, BERT, ResNet-50 and 1D-CNN are used to process text, image and audio data respectively, and the preliminary fusion and spatial alignment of features are realized through cross-modal attention mechanism and full connection layer. The task layer combines the main task loss and modal alignment loss, while the optimization layer adopts adaptive regularization and dynamic weight adjustment strategies to improve the training efficiency and generalization ability of the model. In the experimental part, this paper conducts extensive comparative experiments on CMU-MOSEI and MOSI data sets. The results show that the proposed method is superior to the existing benchmark models such as TFN, MFN and Multimodal-BERT in accuracy, F1 value and reasoning efficiency. In addition, ablation experiments verify the key role of cross-modal attention mechanism and dynamic weight adjustment strategy in improving multi-modal information fusion ability and model adaptability. The comparative analysis of optimization strategies further shows that AdamW optimizer has achieved the best balance between training time and performance gain.