Related Experiment Video
Updated: Sep 5, 2025
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
559
Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network
Nan Zheng1, Meng Sun2, Ye Yang3
1School of Physical Education and Training, Shanghai University of Sport, Shanghai 200438, China.
Computational Intelligence and Neuroscience
|July 11, 2022
Summary
This study introduces EduRedar, a visual analytics system using knowledge graphs and machine learning to analyze college students' sports performance. It helps visualize performance changes and correlate behavior with achievements.
Area of Science:
- Sports Science
- Data Science
- Computer Science
Background:
- College students' sports performance analysis is crucial for understanding physical education effectiveness.
- Existing methods often lack integrated approaches for correlating behavioral data with performance outcomes.
- Visual analytics offers potential for interactive exploration of complex student datasets.
Purpose of the Study:
- To design and implement EduRedar, a visual analytics system for college students' sports performance.
- To integrate multimodal knowledge graphs and predictive models for enhanced data analysis.
- To visualize changes in sports performance and behavior using interactive graphical views.
Main Methods:
- Utilized a multimodal knowledge graph optimized neural network for data analysis.
- Employed HugeGraph for distributed storage of domain knowledge.
- Developed a server-side framework with Spring Boot and a front-end with Vue.js and vis.js for visualization.
- Applied machine learning predictive algorithms to build and optimize performance models.
- Integrated predictive models with interactive visualization for exploring student behavior and performance.
Main Results:
- Successfully designed and implemented EduRedar, a visual analytics system for sports data.
- Demonstrated the capability to store domain knowledge in a knowledge graph and visualize relational networks.
- Enabled multidimensional and multiangle analysis of college students' sports data.
- Visualized changes in sports performance and behavior based on accurate campus exercise data.
Conclusions:
- The proposed visual analytics framework effectively combines predictive modeling with interactive visualization.
- EduRedar supports comprehensive analysis and visualization of college students' sports performance and behavioral changes.
- This approach provides valuable insights for improving physical education strategies and student well-being.
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