Unbalanced incomplete multiview data are widely generated in engineering areas due to sensor failures, data acquisition limitations, etc. However, current research works are rarely focused on unbalanced incomplete multiview unsupervised feature selection (MUFS). To address this issue, this article proposes an MUFS method called unbalanced incomplete multiview unsupervised feature selection with low-redundancy constraint in low-dimensional space (UIMUFSLR). Specifically, the proposed method mitigates the impact of missing samples by learning a unified graph with assigning weights of samples adaptively. In addition, a novel regularization is designed by utilizing the inner product of selected features to obtain low redundancy. An iterative optimization algorithm is devised for UIMUFSLR, accompanied by a comprehensive analysis of its convergence behavior and computational complexity. Experimental results demonstrate the competitiveness of UIMUFSLR in handling unbalanced incomplete multiview data on seven public datasets.