The invention provides a classification method based on a kernel feature extraction early prediction multivariate time series category according to early prediction multivariate time series classification. To extract the essential features of variable time series, first the variable time series undergo feature extraction respectively, and a clustering method is adopted to reduce redundancy features, remove noise and improve classification stability; then, to improve classification efficiency, precision and early degree, a method for comprehensively evaluating feature performances is provided on the basis of accuracy rate, recall rate and the early degree and the like, and the optimal feature in each cluster is selected to serve as a kernel feature of a variable; and finally, two simple effective classifier construction methods are provided on the basis of a kernel feature set of each variable. Correctness and effectiveness of the method and an algorithm are proven through experiments, and experiment results prove that a classifier can have high accuracy rate and good early degree.