Construction of a Smart Educational Platform for Red Culture Based on Big Data Analytics

In accordance with the expanding need for intelligent systems of ideological education, this article suggests a smart learning platform for red culture that is developed on top of big data analytics and recommendation algorithms. Through multimodal user interaction logs, sentiment analysis, and graph-based knowledge models, the platform dynamically maps red cultural materials with cognitive patterns and emotional responses of learners. The platform combines collaborative filtering, symbolic graph traversal, and natural language generation for personalization with ideological fidelity. Experimental trials on real datasets from university systems and cultural archives show considerable increases in engagement, recommendation quality, and content recall. The architecture proposed here provides a practical, scalable system for value-driven education innovation in learning environments with high volumes of data.

Authors

Institutions

Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1145/3744464.3744484
Primary Topic
AI and Big Data Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction of a Smart Educational Platform for Red Culture Based on Big Data Analytics

Shuhan Yang, R. P. Wang, Qi Lu, Xu Ping
AI and Big Data Applications
article

Construction of a Smart Educational Platform for Red Culture Based on Big Data Analytics

Shuhan Yang, R. P. Wang, Qi Lu, Xu Ping
article en

Abstract

In accordance with the expanding need for intelligent systems of ideological education, this article suggests a smart learning platform for red culture that is developed on top of big data analytics and recommendation algorithms. Through multimodal user interaction logs, sentiment analysis, and graph-based knowledge models, the platform dynamically maps red cultural materials with cognitive patterns and emotional responses of learners. The platform combines collaborative filtering, symbolic graph traversal, and natural language generation for personalization with ideological fidelity. Experimental trials on real datasets from university systems and cultural archives show considerable increases in engagement, recommendation quality, and content recall. The architecture proposed here provides a practical, scalable system for value-driven education innovation in learning environments with high volumes of data.

Wuhan Business University (CN), Henan Normal University (CN)
Openalex Percentile: Top 76%
AI and Big Data Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.