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 has 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 DLSR based inductive transfer learning method (TDLSR) is proposed. Based on the framework of inductive transfer learning, the proposed method uses a knowledge leverage mechanism to transfer knowledge from source domain and combines the data in target domain for model learning. TDLSR protects the security of the data in source domain on the basis of guaranteed performance. TDLSR inherits the characteristics of DLSR, which is better applicable to multiclass classification tasks by enlarging the distance between different classes. Compared with DLSR, TDLSR has transfer ability which ensures the learned model can be more reasonable, thus it can be well applied to various complex multiclass classification tasks. Experiments on 12 real UCI datasets verify that the proposed method has good experimental results in response to the above challenges.