The correlation and redundancy of the pathological voice features, which is assorted to the feature set by the random or artificial combinations of these features, always affect the detection effect of the voice. In this paper, we present a method of optimization of pathological voice feature based on KPCA and SVM. Thus, the feature parameters are processed, the correlation and redundant information eliminated, and the representable information extracted for recognition by KPCA. Our experiments based on KPCA show that the highest recognition rate of vowel /a/ is 97.47%, the average recognition rate 91.85%, while these two rates of vowel /i/ are 91.39% and 84.15% respectively. Compared with the traditional combination method, the average recognition rate has effective improvement in our experiment based on KPCA.