Nowadays, adolescence physical health played a vital role in sports to sustain their field else they are terminated from the beginning stage. Moreover, low-level physical fitness level leads to create several health problems for the sportsman. This paper introduces the effective and intelligent deep learning-based algorithm to examine the adolescent physical health and fitness level. Initially, adolescent information such as age, body fat, body mass index, lean body mass, years to peak height velocity, flexibility, jumping information, aerobic fitness level, sprinting, and other physical fitness information is collected. The gathered information is analyzed using a spiral optimized deep neural network because it consists of a large volume of adolescent information collected from the previous analysis. The successful deep learning concept based examination reduces the deviation in adolescent fitness level. With the predicted physical fitness level's help, adolescence is promoted to the next level in sports. In addition to this, the process helps to provide the players' list for improving their efficiency in the training period. The efficiency of the introduced fitness examination system is evaluated using experimental results and analysis.

如今，青春期身体健康在体育运动中起着至关重要的作用，以维持其领域，否则他们将从开始阶段就被终止。而且，低水平的身体健康水平导致运动员产生一些健康问题。本文介绍了一种有效且智能的基于深度学习的算法来检查青少年的身体健康和健身水平。最初，收集青少年信息，例如年龄，脂肪，体重指数，瘦体重，峰高速度的年限，柔韧性，跳跃信息，有氧健康水平，短跑和其他身体健康信息。收集的信息使用螺旋优化的深度神经网络进行分析，因为它包含从以前的分析中收集的大量青少年信息。成功的基于深度学习概念的考试减少了青少年健身水平的偏差。在预测的体能水平的帮助下，运动中的青春期将提升到一个新的水平。除此之外，该过程有助于提供运动员名单，以提高他们在训练期间的效率。使用实验结果和分析评估引入的体格检查系统的效率。