Psychological State Assessment and Training Optimization of Tennis Players Based on Deep Learning

The emotional state of a person is complex and subtle, often expressed subtly through various nonverbal means such as facial expressions, gestures, and tone of voice. However, in practical situations, these emotional signals are often difficult to directly observe or be intentionally or unintentionally misled, making accurate identification of an individual’s true psychological state a challenge. With the rapid advancement of artificial intelligence (AI) technology, deep learning (DL), as one of its core driving forces, has shown unprecedented potential and advantages in multiple fields, including sports science. This paper proposes a DL based psychological state assessment algorithm for tennis players, a high-level competitive group that highly relies on psychological qualities. This algorithm utilizes DL technology to deeply analyze athletes' facial expressions, body language changes, and physiological index fluctuations during competitions, thereby achieving accurate evaluation of athletes' psychological states. Based on the evaluation results, personalized training programs can be further tailored for tennis players. The experimental results show that the algorithm proposed in this paper can accurately evaluate the psychological state of athletes and improve their psychological adaptability and competition performance.