Parkinson’s disease is a widespread neurodegenerative disease that slowly impairs the motor and certain cognitive functions of patients. It is insidious and incurable and can cause a significant burden on sufferers and their families. However, clinical diagnosis of Parkinson’s disease typically relies on subjective rating scales, which can be influenced by the examinee’s recall bias and assessor subjectivity. Numerous researchers have investigated the physiological aspects of Parkinson’s disease from multiple modalities and have provided objective and quantifiable tools for auxiliary diagnosis. However, given the diversity of neurodegenerative diseases and the similarities in their effects, it remains a problem among unimodal methods built upon the representations of Parkinson’s disease to identify the disease uniquely. To address this issue, a multimodal auxiliary diagnosis system comprising the paradigms that evoke aberrant behaviors of Parkinson’s disease is developed in this study. First, parametric tests of the features are performed based on the results of the normal distribution test, and statistically significant feature sets are constructed (p<0.05). Second, multimodal data are collected from 38 cases in a clinical setting using the MDS-UPDRS scale. Finally, the significance of different feature combinations for the assessment of Parkinson’s disease is analyzed based on gait and eye movement modalities; the high immersion triggered task paradigm and the multimodal Parkinson’s disease auxiliary diagnosis system are validated in virtual reality scenarios. It is worth noting that it only takes 2–4 tasks for the combination of gait and eye movement modalities to obtain an average AUC of 0.97 and accuracy of 0.92.