The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a -nearest neighbor-based ID3 optimization algorithm, which selects values similar to -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining.

中文翻译：

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体育比赛动作数据挖掘应用中的在线数据迁移模型和ID3算法

ID3算法是现有数据挖掘中的关键和重要方法，其规则简单易懂，具有较高的应用价值。如果将决策树算法应用于体育比赛动作的在线数据迁移，可以在海量数据之间的关系中把握体育比赛规则，指导体育比赛。分析了传统ID3算法在体育赛事动作在线数据迁移中的应用性能；实现了传统ID3算法的应用步骤和数据处理过程，包括原始数据采集、原始数据预处理、数据准备、决策树构建、数据挖掘，以及对传统ID3算法的综合评价；并澄清了传统ID3算法的问题。主要是澄清了属性缺失和过拟合的问题，为后续算法优化提供了方向。那么，本文提出了一个-基于最近邻的ID3优化算法，对于传统ID3算法的属性缺失问题，选择类似于-最近邻的值来填充缺失值。在此基础上，将改进算法应用于体育比赛动作的在线数据迁移，并评估应用效果。结果表明——基于最近邻的ID3优化算法得到显着改进，同时也可以解决传统ID3算法存在的过拟合问题。对于六类出行方式样本的整体分类问题，实验数据样本具有数据质量高、样本数量可观、样本分化明显等特点。因此，本文还利用基于深度学习的深度分解机算法，利用之前提取的相关特征，对体育比赛动作数据的六类出行方式进行分类。本文的研究为体育比赛动作数据挖掘提供了更准确的方法和更高性能的在线数据迁移模型。