# Student Behavior Prediction of Mental Health Based on Two-Stream Informer Network
## 2 Related Work  
### Education Data Mining  
In recent years, data mining technologies have been used to analyze and mine data generated by students at school with remarkable results, and the results of data mining have been widely applied in education and teaching [3]. These studies analyzed students' activity habits [4; 5; 6] and consumption behavior [7; 8; 9], explored the relationship between students' activities and grades [10; 11], explored poor students [12; 13], etc. [PERSON] et al. [14] collected students' campus behavior data, designed a prediction framework for academic performance based on multi-task learning ranking, and experimentally found that student characteristics such as diligence and regularity were closely related to academic performance. [PERSON] et al. [15] predicted the final grades of students in four private universities through various classification and clustering algorithms in a data mining study. [PERSON] et al. [16] applied a KMeans clustering algorithm to students' consumption behavior, and used an Apriori algorithm to explore the correlation between students' consumption behavior and consumption performance. [PERSON] et al. [17] conducted a questionnaire survey and characterized four university freshman dormitories to study the role of student happiness and empathy on interpersonal relationships, and to mine and analyze students' social networks. [PERSON] et al. [18] designed a KMeans clustering algorithm based on density division and applied it on the Spark framework to divide students into different groups and formulate corresponding management methods for each group.  
The state of students' mental health has been a problem that cannot be ignored, and many experts and scholars have analyzed and researched this issue and proposed related methods. [PERSON] and [PERSON] [19], in the context of the current situation of college students' mental health education, analyzed the factors affecting mental health education through cognitive calculation. They found that students' daily lifestyles were positively correlated with mental health, and proposed corresponding solutions. [PERSON] et al. [20], through the universality and treatment of students' psychology and spirit, understood the developmental stages of students' psychological abnormalities and the uniqueness of their environment, and described the persistence of college students' mental health problems and their influencing factors. [PERSON] et al. [21] conducted a 9-month survey on inter-personal relationships and other life factors of students with mental problems, and found that students with mental abnormalities had less communication with others, tended to ignore the friendly behaviors of others and suffered higher pressure in daily life and learning processes. These investigations and studies on students' mental health provide clear research ideas for the study of students with psychological abnormalities in this paper.  
In summary, researchers have analyzed students' academic performance and social relations, and aimed to identify students with deficits through different dimensions of data and different methods, explored the correlation between students' behavior and their mental health and found students with psychological abnormalities with the aim to provide timely psychological counseling and intervention.