PSYCHOLOGICAL ADJUSTMENT OF COLLEGE COMMUNITY SPORTS STUDENTS THROUGH BIG DATA MODELING

Abstract

College sports students often face unique challenges, including high-pressure competition, academic demands, and social dynamics. Effective psychological adjustment is essential for coping with these stresses and achieving personal and athletic goals. This paper analyzes the possible psychological situation of college students and puts forward how physical education teachers can help students adjust their psychology. According to the intelligent needs of college sports students' psychological state assessment, the convolution results are de-linearized by activation function, and then pooled to improve the nonlinear fitting ability of the network. Use CNN_RNN (Convective Neural Network-Recurrent Neural Network) of DL (Deep learning) to extract text information, and make the model pay attention to the content related to the use of metaphor in the text through the mechanism of metaphorical attention. The results show that the prediction effect of single factor is far lower than that of core factor set, and the prediction accuracy of core factor set can reach over 90%. The experimental results show that the algorithm has certain advantages in predicting the user tasks of MH problem. It can provide theoretical guidance for physical education teachers to help students adjust their psychology.