College Students' Psychological Prediction Algorithm Based on Internet Big Data Mining

Internet big data mining is a new research direction of data mining. It focuses on the analysis of a large amount of data on the Internet, such as web pages, images, videos, etc. The scope of this research direction has been extended to many fields including social sciences and humanities. This paper will discuss how to use machine learning technology to build college students' psychological prediction Internet big data mining. The mental prediction college students is mining on the Internet. Students' personal information, such as age, gender and academic background, is collected from the school's public website. The first step in building an algorithm to predict a person's psychological state is to collect some relevant information about them. Then, five personality models are used to calculate students' personality characteristics (costa& McCrae, 1992). According to the calculation of students' personality, students will be divided into two categories: “good” and “bad”. Bad children will be further divided into four groups according to their scores in different psychological tests, including NEO-PI-R (costa& mccrae, 1992) and Eyseck personality problems.