Parkinson's disease is a widespread neurodegenerative disease that slowly impairs motor and certain cognitive skills. It is insidious and incurable, and it causes 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 studies have used diverse methods to investigate the physiological aspects of Parkinson's disease and have provided objective, quantifiable tools for auxiliary diagnosis. However, given the diversity of neurodegenerative illnesses and the similarities in their effects, it remains a problem among unimodal methodologies built upon the representations of Parkinson's disease to identify the disease uniquely. To this end, we develop a multimodal diagnostic tool comprising the paradigms that evoke potential Parkinson's aberrant behaviors. First, parametric tests of the identifying 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 diagnostic tool are validated in virtual reality scenarios. It is worth noting that it only take 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.