DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM

ABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

摘要
引言 深度学习算法作为一种新型多层网络机器学习算法的新近发展，解决了传统训练算法易陷入极小值点的问题，成为机器学习领域的前沿研究方向。
目的 设计并验证一款人工智能模型，用于深度学习每周负荷训练对学生生物系统造成的影响。
方法 本文结合训练过程中运动员的生理生化指标，对年度备战周期内运动员训练负荷的实际数据展开分析，探讨备战周期内运动员训练负荷的特征；同时围绕深度学习算法在周训练负荷密度的取值、意义、构成要素、编排原则与计算方法，以及判定方式等方面展开论述。
结果 研究结果显示，每日24小时随机抽样的训练负荷涵盖中等强度、低强度与高强度训练，且能够提升机体运动系统与神经系统的反应性。
结论 本研究表明，体育教育与运动训练中可存在“授课”与“训练”两类活动；运动生物学监测研究被证实为运动训练研究的新增长点，具备广阔的未来拓展研究潜力。
证据等级为II级；治疗性研究——治疗效果的相关调查。