Prediction of psychological intervention for college students in digital entertainment media environment based on artificial intelligence and parallel computing algorithms

Abstract

In the era of digital entertainment media, the rapid dissemination of information and the widespread application of social media have a huge impact on user behavior and psychology. For students, the experience of digital entertainment content may have potential adverse effects on their mental health. The aim of this study is to propose a prediction model of university students’ psychological intervention based on optical network transmission based on parallel computing algorithm, in order to improve the computational efficiency and accuracy of the model. Then, a large number of psychological data of college students are collected, and feature extraction and model training are carried out by using machine learning algorithms. Finally, the predictive model of psychological intervention is simulated by parallel computing algorithm. The experimental results show that the optical network transmission based on parallel computing algorithm has high computational efficiency and accuracy in the prediction model of psychological intervention of college students. The model can reliably predict and evaluate the effect of psychological intervention of college students, provide an effective simulation tool for psychological intervention of college students, and help realize personalized and efficient psychological intervention measures.