With the development of the times and changes in society, the social environment faced by college students is constantly changing. Their own learning initiative, hobbies, academic performance, the expansion of the gap between the rich and the poor in the family, and the pressure of employment competition are all in the past. Different characteristics, the factors that induce their mental health problems also show more complex and diverse characteristics, Therefore, it is necessary to use a lot of data technology to conduct in-depth research on their mental health problems. This article aims to study the design and implementation of an intelligent BD-based mental health analysis system for colleges and universities(CAU). In this study, when analyzing the causes of mental health problems, a comprehensive use of various data and related analysis techniques for in-depth mining and analysis, to make the results of the analysis more logical and effective. This article first analyzes the basic principles of data mining and data warehousing in detail, group analysis methods, anomaly extraction methods, and correlation algorithms, and then designs an algorithm-based algorithm-based mining anomaly algorithm for fast data retrieval. At this point, students who may have mental health problems can be identified. Analysis of logic based on real data can provide a reliable basis for psychological teachers, thereby improving the efficiency and effectiveness of school psychological counseling. Experimental research results show that the system has the most accurate analysis of abnormal eating and sleep states, with 91.61%, and the lowest detection accuracy in psychotic abnormal states, with only 61.28%. In abnormal states such as depression, anxiety, paranoia, and horror, The test results are relatively accurate, about 80%. It is still necessary to strengthen the overall configuration and design of the system to maximize its value in practical applications.

Zhijun Wu. Intelligent Analysis System for Ideological Guidance Education and Mental Health in Colleges and Universities Based on Big Data. Frontiers in Educational Psychology (2021), Vol. 2, Issue 4: 1-8. https://doi.org/10.38007/JEP.2021.020401.

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