Content System of Physical Fitness Training for Track and Field Athletes and Evaluation Criteria of Some Indicators Based on Artificial Neural Network

Track and field is a competitive sport with a long history, and it is one of the earliest sports that started scientific training. It is also the main source of many classic theories and methods of modern competitive training, and most of them are still in use today. The purpose of this paper is to study how to analyze and discuss the content system of physical fitness training for track and field athletes and some evaluation criteria of indicators based on artificial neural network. It also describes the BP neural network. This paper puts forward the problem of training content system and index evaluation standard, which is based on artificial neural network. Therefore, the concept and related algorithms are elaborated, and the case design and analysis are carried out. The experimental results show that the physical training method is mainly based on the transformation training method. The goal is to improve the athlete’s training motivation, interest, and adaptability. The training method is relatively simple in design. According to the distribution of muscles, there are more core and trunk training methods, accounting for 23.64% of physical training.