Abstract: For terahertz inverse synthetic aperture radar (ISAR) imaging of space targets, it has significant advantages in achieving high-resolution visualization and all-weather space situation awareness. However, traditional fast imaging methods usually use fast Fourier transform for target image inversion, which will seriously reduce the imaging quality under high dynamic range conditions. To overcome this limitation, a framework based on the adaptive alternating direction method of multipliers (ADMM) is proposed in this study. First, a compressed sensing (CS) model is constructed to complete ISAR imaging reconstruction, and the ADMM algorithm is used to optimize the CS model. Next, an adaptive weighting scheme is introduced to adjust the intensity of the regularization term l₁, aiming to alleviate the imaging accuracy loss caused by traditional fast algorithms. It is worth noting that the ISAR imaging algorithm is derived into a two-dimensional matrix form to reduce memory consumption and improve computational efficiency. Eventually, experimental verification using simulated and field-measured ISAR data shows that the proposed method outperforms the currently existing methods. It achieves improved peak sidelobe ratio and integrated sidelobe ratio in both range and azimuth directions, as well as lower image entropy. Due to its excellent sidelobe suppression capability, the ISAR images processed by this method will show a significant improvement in target detection accuracy.