The Exploration of Integrated Learning Algorithms in the Comprehensive Evaluation of College Students’ Mental Health and the Optimization of Education Management

Faced with the constantly emerging problems of mental health among college students, in order to comprehensively understand the current situation and development needs of college students’ mental health, many educators and researchers propose to study the comprehensive evaluation effect of mental health, in order to judge the changes in college students’ mental health and its role in optimizing educational management. Under the influence of educational ethics concepts, this paper uses ensemble learning algorithms to comprehensively evaluate and predict the mental health of college students, and explores the optimization of educational management. The first aspect is investigating the development and changes in mental health education for college students in recent years and conducting data analysis on the relevant factors involved in the comprehensive assessment of mental health. There is also a need to reorganize the current psychological crisis among college students and the problems faced in optimizing educational management. The second aspect relates to extracting mental health test data after data cleaning and standardized processing, using ensemble learning algorithms for multiple rounds of iterative training to improve data processing efficiency, and establishing a comprehensive prediction model for mental health. This model must be used to validate the algorithm on randomly selected student mental health test data. By using normalization methods and ensemble learning algorithms for sustainable partitioning, the comprehensive evaluation model is optimized to achieve a combination of qualitative and quantitative data analysis. Last aspect is using the integrated learning algorithm to handle imbalanced data and optimize education management. The comprehensive assessment data of college students’ mental health must be incorporated into the optimization of educational management to enhance the effectiveness of management optimization. The research results indicate that ensemble learning algorithms under the influence of educational ethics have good efficacy in processing comprehensive assessment data of college students’ mental health, and can provide reliable assistance for optimizing educational management.