Content area

Abstract

With the continuous development of the social economy, the ways people obtain news information are becoming increasingly diversified, but with that comes too much data. The research on Extracting useful information from too much data is extremely effective. Given these needs and deficiencies, this paper introduces a conditional random field knowledge recognition algorithm, designing a Segmentation analysis model for audience news with the segmentation technology of key frames and shots by sorting the business logic of automatic news Segmentation, realizing the analysis of the news video picture., and then analyze the news Segmentation to ensure that the production and dissemination of news programs are intelligent and smart. The simulation experiment results show that the conditional random field knowledge recognition algorithm is effective and can effectively support the analysis of automatic news Segmentation.

Details

Title
Analysis of automatic news segmentation combining with conditional random field knowledge recognition algorithm
Author
Xiao, Chenghong 1 

 Jilin Province Economic Management Cadre College, Changchun City, China 
Publication title
Volume
18
Issue
4
Pages
3867-3875
Publication year
2024
Publication date
Jun 2024
Publisher
Springer Nature B.V.
Place of publication
Heidelberg
Country of publication
Netherlands
Publication subject
ISSN
18631703
e-ISSN
18631711
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2024-02-27
Milestone dates
2024-01-24 (Registration); 2023-11-26 (Received); 2024-01-24 (Accepted); 2024-01-08 (Rev-Recd)
Publication history
 
 
   First posting date
27 Feb 2024
ProQuest document ID
3256978403
Document URL
https://www.proquest.com/scholarly-journals/analysis-automatic-news-segmentation-combining/docview/3256978403/se-2?accountid=208611
Copyright
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Last updated
2025-10-04
Database
ProQuest One Academic