The goal of Shapelet discovery is to find the Shapelet with the best quality, and the quality of Shapelet depends on the discriminability of the subsequence. Aiming at the problem of accurately discovering effective Shapelet, a fast discovery algorithm of Shapelet based on subclass clustering and SAX representation is proposed, which combines subclass clustering with classical symbolic representation SAX method to obtain the optimal Shapelet quickly and accurately. The algorithm uses subclass clustering to reduce the dimension of time series and obtains multiple subsequence prototypes as Shapelet candidate sets. SAX representation is used to symbolize the candidate set, and the candidate set is intuitively represented by string, which is convenient to find the optimal Shapelet.Finally, the Shapelet with the largest information gain in the candidate set was selected as the optimal Shapelet for time series classification. Experimental results show that this algorithm has good classification effect and improves the classification speed.