Multi-Target Regression (MTR) is a regression problem of single samples with multiple continuous outputs. The existing multi-target regression algorithms learn regression models based on a same feature space, and ignore the specific characteristics of each output target. To solve the problem, a feature construction algorithm for multi-target regression via radial basis function was proposed. Firstly, clustering was applied to each output target with the output of each target as the additional feature, and according to the centers of clusters, the bases of target specific feature space were constructed in the original feature space. Secondly, the radial basis function was utilized to map the original feature space into the target specific feature space, constructing the target specific features, and then a base regression model was built for each target based on these target specific features. Finally, the low-rank learning method was applied to explore and utilize the correlation between the output targets from the latent space formed by the outputs of base regression models. Experiments were conducted on 18 multi-target regression datasets, and the proposed algorithm was compared with some classical regression algorithms, such as Stacked Single-Target (SST), Ensemble of Regressor Chains (ERC) and Multi-layer Multi-target Regression (MMR). The results show that the proposed algorithm outperforms the comparison algorithms on 14 datasets and achieves the best average performance on 18 datasets. It can be seen that the target specific features can improve the prediction accuracy of each output target and improve the overall prediction performance of multi-target regression by combining the low-rank learning to learn and obtain the correlation between the output targets.