Method and system for automatically generating a video based on unstructured knowledge using multi-agent

By using a multi-agent system to evaluate data source weights and calculate semantic similarity of unstructured knowledge materials, controversial knowledge units in the video generation process are identified and processed. This solves the problems of knowledge authenticity identification and content quality control in automatic video generation, and achieves high-quality video output.

CN121418636BActive Publication Date: 2026-07-21KEBAIWEN (SHENZHEN) TECH CO LTD
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
KEBAIWEN (SHENZHEN) TECH CO LTD
Filing Date
2025-12-02
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing automatic video generation technologies lack mechanisms for assessing the authenticity and reliability of knowledge, which may result in generated video content containing incorrect or false information, affecting the video's authority and credibility, and making it difficult to achieve intelligent matching between knowledge credibility assessment and video content presentation.

Method used

A multi-agent system is used to evaluate the data source weights of unstructured knowledge materials. Repeated or contradictory knowledge fragments are identified through time verification and content semantic similarity calculation. A list of disputed knowledge units is generated, and the semantic vector similarity algorithm is used to analyze the matching degree between the disputed units and the preset video script framework. Combined with the backup knowledge base replacement algorithm, a high-quality video output is generated.

Benefits of technology

It achieves end-to-end quality control from knowledge identification to video generation, improving the accuracy and credibility of multi-source knowledge fusion videos and ensuring the authenticity and authority of video content.

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Abstract

The application discloses a method and system for automatically generating a video based on unstructured knowledge of multiple agents, relates to the technical field of video automatic generation based on knowledge processing, and automatically identifies repeated or contradictory knowledge fragments through a data source weight evaluation mechanism and a content semantic similarity calculation method, establishes a controversial knowledge unit list, and adopts a semantic vector similarity algorithm to analyze the matching degree of controversial content and a preset script.For the identified controversial knowledge points, a backup knowledge replacement algorithm is used to retrieve high-confidence replacement content from a reliable knowledge base, a knowledge unit replacement operation is performed, a high-quality video output with a controversial identifier is generated, and the content accuracy and reliability of the multi-source knowledge fusion video are effectively improved.The application realizes a whole-process quality control system from knowledge identification to video generation, realizes intelligent matching of knowledge reliability evaluation and video content presentation mode, and restricts practical deployment of the technology.
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