 Virtual reality, VR, technology offers a great opportunity to explore stress disorder therapies. We created a VR stress training system which incorporates three highly interactive stressful scenes to elicit stress and demonstrate the concurrent variations between physiological data, heart rate, electrodermal activity and eye blink rate, and self-reported stress ratings through a self-designed customized perceived stress questionnaire, SSAI, and wearable devices. Several supervised learning models were rigorously applied to automate stress recognition. Our findings include the evaluations of the VR system by computing Cronbach's Alpha, 0.72, and Kaiser-Meier-Alken, KMO, Coefficient, 0.78, through a retrospective survey which were subsequently confirmed as reliable on four aspects, sense of presence, sense of space, sense of immersion, and sense of reality by a factor analysis. Additionally, we demonstrate the effectiveness of physiology-based stress level classification, no stress, low stress and high stress, and continuous SSAI score prediction, with accuracy reaching 0.742 by bagging ensemble learning model and goodness-of-fit reaching. This article was authored by Kwantau, Wuhan Huang, Yinfei Shen, and others. We are article.tv. Links in the description below.