Fuzzy C-Means Clustering Algorithm in College Students' Mental Health Test

Human resources have generated massive and diverse data. When it comes to big data, the most important feature of data minin (DM) is their uniqueness. This article aims to study the application of fuzzy C-means clustering algorithm in college students' mental health test. This article describes the development and application of aggregation analysis algorithms, identifies the most common distance types and aggregation tasks in aggregation, and describes some classic aggregation analysis algorithms in detail. On the basis of explaining the advantages and disadvantages of the speed analysis algorithm and the algorithm compilation process, we propose a two-step concept, discuss the algorithm flow of the aggregation model in detail and discuss the diagram, using the calculation algorithm to analyze the mental health assessment tools such as UPI Student mental health data created to create DM models and mine student groups and their components, research about student psychology, summarize student and mental health, recovery and anxiety, introversion and extroversion, emotion and calm and cautious, fear and boldness The relationship between. In order to facilitate the school to formulate new methods to prevent students' psychological problems, it will create a positive and healthy environment for college students to study and live. Experiments have shown that in the results of the college students' mental health test analyzed in this article, the psychological problems of male college students are relatively more prominent, while 80% of college students have normal mental health.