In this paper, we propose a stroke clustering-based coarse classification mechanism to classify the multi-fonts Chinese characters. The main purpose of the proposed method is to identify the associating type of an input character together with the extraction of its embedded composing components. In this paper, the K-mean clustering algorithm is employed to cluster the thinned strokes. Besides, mis-clustered stroke modification techniques are developed to rearrange the mis-clustered strokes generated by the K-mean algorithm. Five kinds of fonts for 2500 frequently used Chinese characters are tested in our experiments. The average classification rate is 92.57% which is very promising for coarse classification.