In view of the problem of the early intelligent diagnosis for Parkinson's Disease (PD) which occurs more common in the elderly, the clustering technologies based on medical detection text information data were proposed for the analysis and prediction of PD. Firstly, the original dataset was pre-processed to obtain effective feature information, and these features were respectively reduced to eight dimensional spaces with different dimensions by Principal Component Analysis (PCA) method. Then, five traditional classical clustering models and three different clustering ensemble methods were respectively used to cluster the data of eight dimensional spaces. Finally, four clustering performance indexes were selected to predict PD subject with dopamine deficiency as well as healthy control and Scans Without Evidence of Dopamine Deficiency (SWEDD) PD subject. The simulation results show that the clustering accuracy of Gaussian Mixture Model (GMM) reaches 89.12% when the value of PCA feature dimension is 30, the clustering accuracy of Spectral Clustering (SC) is 61.41% when the PCA feature dimension value is 70, and the clustering accuracy of Meta-CLustering Algorithm (MCLA) achieves 59.62% when the PCA feature dimension value is 80. The comparative experiments results show that GMM has the best clustering effect in the five classical clustering methods when the PCA feature dimension value is less than 40 and MCLA has the excellent clustering performance among the three clustering ensemble methods for different feature dimensions, which thereby provides the technical and theoretical supports for the early intelligent auxiliary diagnosis of PD.