This paper presents a novel framework for the three-dimensional (3D) reconstruction of space targets using a multiview sequence of sparse Inverse Synthetic Aperture Radar (ISAR) imaging. Due to the limited observation windows and high-speed motion of space targets, traditional ISAR imaging often suffers from sparse aperture constraints, leading to degraded image quality. To address this, we utilize a sequence of sparse ISAR images captured from multiple viewing angles, integrating them through a fusion-based reconstruction algorithm. By exploiting the spatial correlation between views and applying compressed sensing (CS) or deep learning-based recovery techniques, the target's 3D scattering centers are accurately estimated. Experimental results on simulated and real-measured data demonstrate that the proposed multiview approach effectively overcomes the limitations of single-view sparse imaging, providing a high-fidelity 3D structural representation of space targets for enhanced situational awareness.