The independency between each dichotomizer trained by coding matrix’s bi-partition is the key to using errorcorrecting output codes(ECOC) to solve multiclass problems. Therefore, an error-correcting output codes method based on feature space transformation(FST) is proposed. Inspired by the ensemble learning theory, a third feature space dimension is introduced into the coding matrix. Then, different subspaces are obtained by feature space transformation based on different positive and negative subclasses, so that the diversity between different binary classifiers are promoted to make the classification performance better. The experiment results based on UCI datasets show that the codes based on FST are better than the original codes. Besides, the proposed method can be applied to any kind of coding matrix, and provides new thought to large dataset for its quick training time and simplicity