Kernel method is a common machine learning algorithm used in classification, clustering, regression and feature selection. Kernel selection and kernel parameter optimization are the crucial problems which impact the effectiveness of kernel method, and therefore motive the research on kernel evaluation measure, especially universal kernel evaluation measure. Five widely used universal kernel evaluation measures, including KTA, EKTA, CKTA, FSM and KCSM, are analyzed and compared. It is found that the evaluation object of five universal kernel evaluation measures mentioned above is average margin of a linear hypothesis in feature space, which has bias against the SVM optimization criterion to maximize minimum margin. Then, this study applies synthetic data to analyze the class distribution sensitivity, linear translation sensitivity, and heteroscedastic data sensitivity. It also concludes that the measures mentioned above are only the unnecessary and sufficient condition of kernel evaluation, and good kernel can achieve low evaluation value. Finally, comparing the evaluation result of the measures mentioned above on 9 UCI data sets and 20 Newsgroups data set suggests that CKTA is the best universal kernel evaluation measure.