Due to the influence of psychological factors during sports, basketball players often change the shooting rhythm in sports, which will reduce the shooting rate and directly affect the training effect. Therefore, we need to analyze the basketball shooting process of some athletes, which is not a complete negation of traditional training methods, but a psychological analysis of some athletes’ shooting in the course of competition, so as to complement the feedback and training information after the game. With the great improvement of computer computing power, deep learning natural language processing can help people analyze and solve previously unsolvable problems in production and life. The psychological emotion of adolescence has a great influence on the study and life of middle school students. At present, the unified monitoring and analysis of the daily life of middle school students need not only a lot of manpower, but also slow speed. If the psychological problems are not found in time and feedback is given, it will cause a series of adverse effects on individual athletes. The neural network model based on deep learning can process students' daily mass text information quickly and accurately, and then give comprehensive judgment, which is a good solution. This paper applies the neural network algorithm of the Bi-LSTM model and CNN model to study the text data, and finally has 95.55% and 90.03% accuracy in the psychological analysis experiment, which provides a feasible solution to solve the batch rapid analysis of psychological changes reflected in the daily text of athletes during basketball. Some suggestions are put forward on how to strengthen and improve the psychological quality of college basketball players and their ability to bear pressure and difficulties.

中文翻译：

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深度学习视角下的篮球运动员心理分析

由于运动中心理因素的影响，篮球运动员在运动中经常会改变投篮节奏，这会降低投篮命中率，直接影响训练效果。因此，我们需要对部分运动员的投篮过程进行分析，这并不是对传统训练方式的完全否定，而是对部分运动员在比赛过程中投篮的心理分析，以补充训练后的反馈和训练信息。游戏。随着计算机计算能力的极大提高，深度学习自然语言处理可以帮助人们分析和解决以前在生产和生活中无法解决的问题。青春期的心理情绪对中学生的学习和生活有很大的影响。现在，对中学生日常生活的统一监控和分析，不仅需要大量的人力，而且速度慢。如果不及时发现心理问题并给予反馈，将对运动员个体造成一系列不良影响。基于深度学习的神经网络模型可以快速准确地处理学生日常海量文本信息，进而给出综合判断，是一个很好的解决方案。本文应用Bi-LSTM模型和CNN模型的神经网络算法对文本数据进行研究，最终在心理分析实验中分别有95.55%和90.03%的准确率，为解决批量快速分析心理问题提供了一个可行的方案。变化体现在运动员在篮球运动中的日常文字中。