Analysis of the timeliness of multiple information on college students’ patriotism education in the all-media era

• Published in: Applied mathematics and nonlinear sciences Vol. 9; no. 1
• Main Author: Meng, Ying
• Format: Journal Article
• Language: English
• Published: Sciendo 01.01.2024
• Subjects: 97U10; Bi-LSTM; Knowledge graph; Multiple information; Patriotism education; SFAJED algorithm
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns.2023.2.01596

This paper explores the process of patriotism knowledge mapping from three aspects: graph entropy definition, entropy2vec and topic knowledge, and constructs the overall framework of patriotism knowledge mapping. The multivariate information technology in the era of all media is used to summarize the educational knowledge of the patriotism theme, construct the entity-relationship extraction model based on the Bi-LSTM network, and propose the SFAJED fusion algorithm to carry out the knowledge fusion of the patriotism education theme. After exploring the relationship extraction effect, knowledge fusion effect, and overall effect of the constructed knowledge graph, the timeliness of patriotism education for college students is analyzed. The results show that the network density of the constructed patriotism knowledge map is concentrated at about 0.004, the aggregation coefficient is concentrated at about 0.45, and the average road strength length of the graph is about 20. It is divided into three stages in accordance with the time axis: 2015~2017 is the patriotism education with class as the main axis, 2017~2018 is the patriotism education with the country as the basis, and 2018~2020 is the patriotism education with the culture as the virtue, and at this time, the strength is respectively between 2.3~3.5, between 2.12~3.08 and between 1.08~2.88.