Anchor-based incomplete multi-view clustering methods utilize anchors to uncover clustering structures. However, relying on anchor graphs for producing final indicators is indirect, which can lead to information loss and suboptimal outcomes. Besides, most methods neglect the potential of anchors for imputing missing views. To address these limitations, we propose a Joint View Imputation and Label Generation (JVILG) method. JVILG comprises the Anchor-based tensorized Label Generation (ALG) module for generating clustering labels and the Anchor-based sparse regularized Subspace Correlation (ASC) module for recovering missing views. The ALG module explicitly connects data observations, the fine-grained anchor matrix, and soft label matrices within a reconstruction framework through a membership matrix, while imposing tensor Schatten p-norm regularization on the constructed label tensor to capture spatial correlations among views. Meanwhile, the ASC module directly uses fine-grained anchors to impute missing data in respective views. By integrating the ALG and ASC modules, JVILG enhances synergy between different tasks and mitigates the impact of missing information on clustering. Experimental results on six datasets demonstrate the effectiveness of JVILG compared to both shallow and deep state-of-the art methods.The code is available at https://github.com/W-Xinxin/JVILG.