【摘要】 在体育训练中，针对不同类型的运动员制定正确的训练计划和执行方案至关重要.模糊建模与免疫算法相结合的机器学习方法可用于优化游泳运动员的训练.综合考虑游泳运动员的能力水平、体能状况、技术熟练程度以及训练目标等多种因素，实验中建立了12个属性的数据集，这些属性被分为离散型和连续型两类.在数据收集过程中，考虑到个体差异和训练过程中的不确定性，对每个属性进行了差异化和连续化处理.使用机器学习方法对游泳训练进行建模，可以更好地理解和指导运动员的训练过程.更多还原

【Abstract】 In sports training, it is very important to make correct training plans and implementation plans for different types of athletes. The machine learning method combining fuzzy modeling and immune algorithm can be used to optimize the training of swimmers. Considering the ability level, physical condition, technical proficiency and training objectives of swimmers, a data set of 12 attributes is established in the experiment, which are divided into two categories: discrete and continuous. In the process of data collection, each attribute is differentiated and continuous to consider individual differences and uncertainties in the training process. By using machine learning method to model swimming training, we can better understand and guide the training process of athletes.更多还原