@article{4447,
  author = {Jianqiang Feng},
  title = {Clustering Mining Method of College Student's Physical Exercise Behavior Characteristics based on Ant Colony Algorithm},
  journal = {Journal of Intelligent Computing},
  year = {2025},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jic/2025/16/2/71-78},
  url = {https://www.dline.info/jic/fulltext/v16n2/jicv16n2_3.pdf},
  abstract = {Physical exercise is essential for the sustainable self-cultivation of college students and is an indispensable part of higher education. However, most college students lack systematic knowledge of physical exercise, making it challenging to exercise scientifically. Therefore, this paper introduces the ant colony algorithm into the model for extracting characteristics of college student's physical exercise behavior to improve the effectiveness of behavior recognition and clustering. Furthermore, the model's performance is enhanced through optimization of the ant colony algorithm. The experimental results demonstrate that the clustering model of college student's physical exercise behavior, based on the ant colony algorithm, effectively reduces the error rate and maintains good accuracy as the sample size increases, indicating good stability and reliability. Additionally, for different physical exercise behaviors, the clustering F-measure standard values of the model all exceed 0.8, indicating a better clustering effect.},
}