Open AccessApplication of Association Rule Data Mining in Statistical Analysis of College Students' Mental Health
Author(s) -
Hongrui Zhang
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
ISSN - 0010-8189
DOI - 10.17762/converter.250
Subject(s) - mental health , big data , computer science , association rule learning , data science , college health , statistical analysis , identification (biology) , psychology , data mining , statistics , medicine , mathematics , family medicine , biology , psychotherapist , botany
Strengthening the application of big data technology in data analysis can effectively improve the service capability and level of relevant statistics, and provide comprehensive and reliable information support for macro decision-making and trend analysis. This paper comprehensively reviews the research status of big data technology in the field of college students' mental health at home and abroad. Combining with the characteristics of college students' mental health statistical data and the weaknesses in statistical analysis, the feasibility of using knowledge mapping technology is demonstrated. On this basis, the blood relationship graph and influence analysis among the statistical indicators of college students' mental health were constructed through the knowledge map. The application of the knowledge map of college students' mental health statistical indicators in statistical data analysis, statistical indicator identification and statistical data quality management is proposed. Specifically, based on the concept of big data, we can establish a decision analysis platform for college students' mental health. Based on the big data technology, the data mining and analysis ability can be enhanced. In addition, it can change the traditional thinking of college students' mental health statistics and strengthen the construction of statistical team.