Intelligent Classification and Content Recognition of Enterprise Financial Archives Based on a Multimodal Deep Learning Mechanism

This study proposed an intelligent classification and content recognition framework for enterprise financial archives based on a multimodal deep-learning architecture. The model integrated image, audio-video, and text modalities, using DenseNet—a 3D densely connected convolutional neural network with openSMILE software features—and a bidirectional-encoder-representations-from-transformers encoder. An attention-guided bidirectional gated-recurrent-unit fusion module modeled intramodal and intermodal dependencies, and a grid-structured classification head preserved spatial and temporal consistency. A class-aware readout with additive-margin softmax strengthened category separation and improved interpretability. Experiments on a dataset of 38,420 multimodal financial records showed that the proposed framework achieved an accuracy of 95.8%, an F1-score of 95.3%, and an area under the curve of 0.981, surpassing unimodal and transformer-based baselines. The results indicated that the multimodal attention–gated-recurrent-unit grid framework effectively captured cross-modal semantics and localized features, offering an efficient approach for financial document analysis and audit automation.