Data Analysis and Feedback System Construction of University Students’ Psychological Fitness Based on Fuzzy Clustering

Abstract

With the advancement of information technology, mega data techniques are becoming more widely used in data analysis applications in all walks of life; the expansion of universities has resulted in a significant increase in the number of students; and the improvement of living standards has caused more parents to travel to other places. Children are becoming less and less communicative. Many students become lonely and worried in this environment, and psychological fitness issues develop over time. We should also introduce the application method of big data into the field of college psychological fitness education to improve the accuracy of college students’ psychological fitness education. Giving priority to university students’ psychological quality education, conducting targeted psychological fitness counseling based on the current state of university students’ psychological fitness, and effectively cultivating high-quality talents are realistic and urgent tasks faced by universities. Universities can use the relevant methods of fuzzy set and cluster analysis to evaluate university students’ psychological fitness in order to provide more relevant psychological fitness education and to study and evaluate university students’ psychological fitness problems more scientifically. We should extract the performance characteristics of university students’ rebellious psychological phenomenon and analyze the relationship between rebellious psychology and behavior obstruction in detail when modeling psychological fitness quality. Traditional methods, on the other hand, suffer from a large modeling error due to the introduction of a mapping table and a misclassification rate threshold. This paper investigates the analysis of university students’ psychological fitness data and the construction of a feedback system using mega data techniques, based on their current psychological fitness status.