Since the effectiveness of traditional machine learning methods depends on a large amount of effective training data and it is difficult to satisfy, transfer learning has been widely studied and become a hot research in recent years. In order to meet the challenge that the classification performance is degraded due to the serious shortage of training data in current multiclass classification scenarios, a discriminative least squares regressions(DLSR) based inductive transfer learning method(TDLSR) is proposed. The proposed method starts with inductive transfer learning, and transfers knowledge from source domain to target domain through knowledge leverage mechanism. It combines the knowledge of source domain and data in target domain for model learning, which improves classification performance and ensures the security of source domain data. The TDLSR inherits the advantage of the DLSR, which is better applicable to multiclass classification tasks by enlarging the distance between different classes, and injects transfer ability for the DLSR to adapt to the challenge of training data shortage. It can be well applied to various complex multiclass classification tasks. Experiments on 12 real UCI datasets verify the effectiveness of the proposed method.