College of Preschool Education and Humanities, Dongguan Vocational and Technical College, Dongguan, Guangdong 523808, China.
J Healthc Eng. 2021 Oct 25;2021:1382559. doi: 10.1155/2021/1382559. eCollection 2021.
With the diversification and rapid development of society, people's living conditions, learning and friendship conditions, and employment conditions are facing increasing pressure, which greatly challenges people's psychological endurance. Therefore, strengthening the mental health education of students has become an urgent need of society and a hot issue of common concern. In order to solve the problems of high misjudgment rate and low work efficiency in the current mental health intelligence evaluation process, a mental health intelligence evaluation system based on a joint optimization algorithm is proposed. The joint optimization algorithm consists of an improved decision tree algorithm and an improved ANN algorithm. First, analyze the current research status of mental health intelligence evaluation, and construct the framework of mental health intelligence evaluation system; then collect mental health intelligence evaluation data based on data mining, use joint learning algorithm to analyze and classify mental health intelligence evaluation data, and obtain mental health intelligence evaluation results. Finally, through specific simulation experiments, the feasibility and superiority of the mental health intelligent evaluation system are analyzed. The results show that the system in the article overcomes the shortcomings of the existing mental health intelligence evaluation system, improves the accuracy of mental health intelligence evaluation, and improves the efficiency of mental health intelligence evaluation. It has good system stability and can meet the actual current situation, which are requirements for mental health intelligence evaluation.
随着社会的多元化和快速发展，人们的生活条件、学习和交友条件、就业条件都面临着越来越大的压力，这极大地挑战了人们的心理承受能力。因此，加强学生心理健康教育已成为社会的迫切需要和共同关注的热点问题。为了解决当前心理健康智能评估过程中误判率高、工作效率低的问题，提出了一种基于联合优化算法的心理健康智能评估系统。联合优化算法由改进的决策树算法和改进的 ANN 算法组成。首先，分析心理健康智能评估的当前研究现状，构建心理健康智能评估系统框架；然后基于数据挖掘收集心理健康智能评估数据，使用联合学习算法对心理健康智能评估数据进行分析和分类，得到心理健康智能评估结果。最后，通过具体的仿真实验，分析了心理健康智能评估系统的可行性和优越性。结果表明，本文提出的系统克服了现有心理健康智能评估系统的不足，提高了心理健康智能评估的准确性，提高了心理健康智能评估的效率。它具有良好的系统稳定性，可以满足实际现状，这是对心理健康智能评估的要求。
