Feature selection is one of the most important dimensionality reduction techniques for its efficiency and interpretation. Recently, some sparse regression-based feature selection methods have obtained an increased attention from the research community. However, previous sparse regression-based feature selection methods are limited by the double-layer structure. To improve the learning performance, in this work, we propose a sparse structural feature selection model for multitarget regression, which utilizes a structure matrix to expand the double-layer to multi-layer structure. Our aim tries to explore the essential inter-target correlations. To enhance the robustness of our proposed method, we emphasize a joint l(2, 1)-norm minimization on the loss function, regression matrix, and structure matrix. An effective optimization method with provable convergence behavior is also proposed. Extensive experimental results on multivariate prediction demonstrate the effectiveness of our proposed method.