Multi-modal file intelligent classification and label generation method and system

By constructing a unified and integrated semantic representation structure and hierarchical tag generation model, the semantic heterogeneity problem of multimodal archive content is solved, achieving highly accurate and structured tag generation and supporting adaptive model optimization.

CN120974229APending Publication Date: 2025-11-18GEOLOGICAL PROSPECTING TECH INST BEIJING
0 Cites 8 Cited by

Patent Information

Application Number
CN202511082515.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-04
Publication Date
2025-11-18

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively handle the semantic heterogeneity and coupling of multimodal archive content, resulting in low accuracy and poor semantic consistency in tag generation, a lack of structured expression capabilities, and a lack of feedback optimization mechanisms, making it difficult to adapt to semantic evolution or changes in the tag system.

Method used

A unified and integrated semantic representation structure is constructed. Through hierarchical label generation model and label graph structure, combined with semantic encoding of multimodal archive content, cross-modal joint modeling and contextual attention extraction, multi-level archive semantic labels with optimizable feedback are generated.

Benefits of technology

It achieves unified semantic modeling of multimodal archives, improves the accuracy of tag generation and contextual understanding, has multi-level structural clarity and feedback optimization capabilities, and supports model adaptation and long-term evolution.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of artificial intelligence and information processing, and particularly discloses a multi-modal file intelligent classification and label generation method and system. The method comprises the steps of obtaining text paragraphs, image segments and video key frame data in a file, performing format recognition and region separation, and generating multi-modal content structure data; semantic features are extracted based on the data, and fused semantic representation is constructed; generating a main label, a sub-label and a keyword set by using the fused semantic vector, and constructing a multi-level label structure; and further performing label redundancy elimination and structure optimization to generate a label atlas, and performing consistency analysis and feedback optimization through the label atlas and the semantic representation structure. Compared with the prior art, the method has the advantages that various modal information can be effectively fused, the accuracy, hierarchical structure and semantic consistency of archive label generation are improved, and the method has high intelligence, self-adaption and sustainable optimization capabilities and is suitable for various scenes such as archive management, content auditing and semantic archiving.
Need to check novelty before this filing date? Find Prior Art