Western scales are a significant basis for assessment of Parkinson′s disease(PD), while these scales contain a large number of cross duplicates scales, which hampers rapid assessment of PD. Therefore, optimizing these wetern scales is significant for rapid diagnosis of PD. And the method of the optimization of Parkinsons scale based on principal component analysis(PCA) is raised. The weighted projective vector is extracted based on principal component analysis, and scale problems are divided on the basis of the projected vector using local recursive segmentation algorithm based on Ostu threshold, Finally, based on contribution factors(CF), a new scale is designed. Experiment results confirm that the new combinations of scale which accounts for 21% of the original western scales is highly comparable to original western scales for identifying PD support vector machine(SVM).