This paper introduces a FKHP-based fuzzy classification model with the proposed learning method of FKHP. For this model, the fuzzy partition and the fuzzy classification rules are automatically generated with the method of kernel function and perceptron. During constructing such model, first, patterns in the initial input space are mapped to high dimensional feature space by selecting a suitable kernel function. In the feature space, the hyper-ball which covers all training patterns of a class is founded for every class by the algorithm of FKHP. A hyper-ball is regarded as a fuzzy partition and a IF-THEN rule is created for a fuzzy partition. Experiments with the data sets of standard machine leaning database evaluate the performances of this model with comparison of experiment results of the methods of kernel hyper-ball perceptron and support vector machine. The proposed model has the fast classification training rate, better astringency and high recognition rate.