Prediction mechanism of depression tendency among college students under computer intelligent systems

• Published in: Journal of intelligent systems Vol. 33; no. 1; pp. 1477 - 86
• Main Author: He, Zhang
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 29.04.2024; Walter de Gruyter GmbH
• Subjects: Academic achievement; Accuracy; College students; Colleges & universities; computer intelligent systems; Data collection; depression tendencies; emotional dictionary; Emotional disorders; Intelligence; Intelligent systems; Mental depression; Natural language processing; neural network; Neural networks; Predictive analytics; predictive mechanisms; Quality of life; Students; System effectiveness
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2023-0209

In response to the existing problems in the prediction accuracy, data collection, real-time monitoring, and consideration of factors leading to depression in the current mechanism for predicting depression among college students, this article used computer intelligent systems to study the prediction mechanism of depression among college students. This article conducted a survey on students at University A using a survey questionnaire to understand the main reasons that affect their tendency to develop depression. It processed and analyzed the data using the Beck Depression Scale and Statistical Product and Service Solution 21.0 (SPSS 21.0). Meanwhile, natural language processing techniques in computer intelligence systems can be utilized. This article combines emotional dictionaries and word frequency-inverse document frequency to construct a prediction mechanism model for depression tendencies among college students, improving the accuracy of predicting student depression tendencies. The experiment shows that the average accuracy of the depression tendency prediction mechanism model constructed based on Natural Language Processing technology after 50 experiments was 97.02%, which was 5.33% higher than the model constructed based on neural network calculations. Overall, research on the prediction mechanism of depression tendency among college students based on computer intelligence systems can provide more effective mental health support and intervention measures for schools, helping students improve their psychological state, academic achievement, and quality of life.