现代信息科技2025，Vol.9Issue(8)：65-70,6.DOI:10.19850/j.cnki.2096-4706.2025.08.013
基于Node2Vec-LGBM模型的CBA球员位置预测
CBA Player Position Prediction Based on Node2Vec-LGBM Model
何家丽 1杨军1
作者信息
- 1. 上海第二工业大学计算机与信息工程学院,上海 201209
- 折叠
摘要
Abstract
With the accumulation of sports data and the rapid development of Artificial Intelligence technology,it is particularly important to use Big Data and Machine Learning methods to optimize player position prediction.However,traditional methods often ignore the complex structural relationships between players,which are crucial for position prediction.Therefore,this paper proposes a player position prediction model based on Node2Vec and Light Gradient Boosting Machine(LGBM).Through data mining and analysis,the basic data of CBA players in three seasons are crawled,and the LGBM model is used to predict the position of players.Combined with hyper-parameter optimization and Node2Vec graph embedding algorithm,the accuracy of the model itself is further improved.The experimental results show that the model can not only effectively optimize the team's lineup and tactical arrangements,but also provide strong support for the team to enhance its competitiveness and overall performance.关键词
机器学习/轻量级梯度提升机/Node2Vec/预测模型Key words
Machine Learning/Light Gradient Boosting Machine/Node2Vec/prediction model分类
信息技术与安全科学引用本文复制引用
何家丽,杨军..基于Node2Vec-LGBM模型的CBA球员位置预测[J].现代信息科技,2025,9(8):65-70,6.
