To address the problem that traditional multitarget regression algorithms only focus on the linear correlation between input features and target outputs, but ignore the structural information between different instances, i.e., instance correlation and target correlation, which leads to limited performance of the algorithm, we propose a multi-target sparse regression algorithm based on instance and target correlation(MTR-ITC). First, we construct latent variable space to decouple the complex input-output and output correlation structures, and impose sparse constraints on the corresponding coefficient matrices to explicit encoding and sparse learning of inter-target correlations in the latent variable space. Then, manifold learning is introduced to explore the correlation between different instances in the input and output space to ensure that the model output is consistent with the real results in terms of local and global structure. Finally, an alternating optimization algorithm is proposed to solve the objective function optimally and converge it to the global optimum efficiently. Experiments on the benchmark test dataset show that the MTR-ITC improves the performance of the algorithm compared to existing representative algorithms, and its good convergence makes it possible to iterate and converge to the global optimum rapidly.