研究目的

本研究旨在分析飞碟射击运动员的人格特质、心理疲劳、自我控制三者与运动成绩之间的相互关系，以揭示这些心理因素对运动表现的可能影响机制，进一步为心理干预和人才选拔提供科学的理论依据。同时结合这三个心理因素，使用机器学习算法构建高精度的成绩预测模型，为教练员和心理服务人员的训练计划和干预策略提供可靠的数据支持，从而最大限度地发挥每位运动员的潜力，提高其运动成绩。

研究方法

选取39名国家飞碟射击队员（男=19，女=20），平均年龄24.92 岁( SD = 5.87)。先收集各个变量的数据进行相关性分析和回归分析，后采用灰色关联分析和支持向量机（SVM）构建成绩预测模型。

研究结果

在五项人格特质中，只有责任性与运动成绩之间存在显著正相关（r = 0.370*，p < 0.05），且能显著预测运动成绩（p < 0.05）。自我控制、心理疲劳与运动成绩分别呈显著的正负相关（r = 0.352*，p < 0.05；r = -0.426**，p < 0.01）。在有关人格、自我控制和心理疲劳的回归模型中，除了责任性所在的模型，其余所有模型中心理疲劳均呈现出显著的负面预测（p < 0.05）。自我控制对运动成绩并未呈现出显著的预测能力，但显示了对人格与心理疲劳的调节效用。将人格与心理疲劳交互后再次进行回归预测，结果显示开放性和责任性与心理疲劳的交互项不显著。此外，与单独回归预测相比，自我控制在交互模型中呈现出了显著的预测作用，且对人格与心理疲劳交互项的调节效用显著增强。最后，结合以上心理变量运用灰色关联分析和SVM构建的成绩预测模型具有较高的预测准确率（RMSE=0.72927）。

研究结论

心理疲劳对运动成绩有着较大的负面干扰，对于教练员和心理服务人员来讲，应该特别关注那些心理疲劳水平较高的运动员，需要及时对他们采取干预措施。开放性和责任性能减少心理疲劳的负面效应。其中，责任性尤为关键，它能直接显著地预测运动成绩。在飞碟运动员的选拔过程中，应更加注重这一特质。此外，当自我控制资源未被显著损耗时，自我控制对心理疲劳有着显著调节作用，并能积极预测运动成绩。在后续的培养阶段，也应着重培养运动员的自我控制能力，这可以帮助运动员更好地管理自己的行为和情感，提高他们在比赛和训练中的表现。最后，结合心理指标应用机器算法构建的成绩预测模型具有较高的准确性，可以在投入竞技环境实际应用，帮助教练员和心理服务人员对运动员做出及时的干预或调整。

Research purpose This study aims to analyze the relationships between personality traits, mental fatigue, self-control, and athletic performance in skeet shooters, to uncover the potential mechanisms through which these psychological factors influence athletic performance. This provides a scientific theoretical basis for psychological interventions and talent selection. Additionally, by integrating these psychological factors, we use machine learning algorithms to construct a high-precision performance prediction model. This model provides reliable data support for coaches and psychological service providers in their training plans and intervention strategies, thereby maximizing each athlete's potential and enhancing their competitive performance.

Research Methods The study involved 39 national team clay target shooters (19 men and 20 women), with an average age of 24.92 years (SD = 5.87). Data collection on various variables was followed by correlation and regression analyses. A performance prediction model was then constructed using Grey Relational Analysis and Support Vector Machine (SVM).

Research Results Among the five personality traits, only conscientiousness showed a significant positive correlation with athletic performance (r = 0.370*, p < 0.05) and was a significant predictor of performance (p < 0.05). Self-control and mental fatigue were significantly correlated with athletic performance in positive and negative directions, respectively (r = 0.352*, p < 0.05; r = -0.426**, p < 0.01). In the regression models concerning personality, self-control, and mental fatigue, mental fatigue showed a significant negative prediction (p < 0.05) in all models except the one including conscientiousness. Self-control did not significantly predict athletic performance but demonstrated a moderating effect on the relationship between personality and mental fatigue. When personality and mental fatigue were interacted and regressed, the interaction terms of openness and conscientiousness with mental fatigue were not significant. However, compared to the separate regression predictions, self-control in the interaction model showed a significant predictive effect and significantly enhanced the moderation of the interaction between personality and mental fatigue. Finally, the performance prediction model constructed using grey relational analysis and SVM, incorporating these psychological variables, demonstrated high predictive accuracy (RMSE = 0.72927).

Research Conclusion Mental fatigue has a substantial negative impact on athletic performance. Coaches and psychological service providers should pay special attention to athletes with high levels of mental fatigue and implement timely intervention strategies. Openness and conscientiousness can mitigate the negative effects of mental fatigue, with conscientiousness being particularly crucial as it can directly and significantly predict athletic performance. In the selection of skeet shooters, more emphasis should be placed on this trait. Additionally, when self-control resources are not significantly depleted, self-control significantly moderates mental fatigue and positively predicts athletic performance. In subsequent training phases, emphasis should be placed on developing athletes' self-control abilities, which can help them better manage their behavior and emotions, thereby improving their performance in competitions and training. Finally, the performance prediction model constructed using machine learning algorithms and incorporating psychological indicators has high accuracy, making it applicable in competitive environments to help coaches and psychological service providers make timely interventions or adjustments for athletes.