Research Article
Data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses
@INPROCEEDINGS{10.1007/978-3-031-33545-7_13, author={Jin Zhou and Wenjuan Xie}, title={Data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses}, proceedings={IoT and Big Data Technologies for Health Care. Third EAI International Conference, IoTCare 2022, Virtual Event, December 12-13, 2022, Proceedings}, proceedings_a={IOTCARE}, year={2023}, month={5}, keywords={Support Vector Machine Higher Vocational Colleges Tourism English Course Ideology and Politics Psychological Tendency Health Data Mining}, doi={10.1007/978-3-031-33545-7_13} }- Jin Zhou
Wenjuan Xie
Year: 2023
Data Mining of Psychological Tendency and Health of Ideological and Political Students in Higher Vocational Tourism English Courses
IOTCARE
Springer
DOI: 10.1007/978-3-031-33545-7_13
Abstract
In order to improve the quality of students’ psychological tendency health data, this paper puts forward the research of mental tendency health data mining based on SVM. By cleaning the students’ mental tendency health data and optimizing the SVM with particle swarm optimization algorithm, the classification of students’ mental tendency health data is completed. According to the weighted results of mental propensity health data, the primary classification and feature recognition of large-scale mental propensity health data were carried out. Based on the optimal modeling of students’ psychological tendency health data, the objective function of maximum difference of students’ psychological tendency health data is defined. Maximum likelihood estimation is used to obtain the frequency distribution of the data in the Tourism English Course of higher vocational colleges. Experimental results show that the proposed method can not only improve the accuracy and efficiency of mental health data classification, but also control the integrity of mental health data over 80%.
