Multi-view data obtained from different perspectives are becoming increasingly available. As such, researchers can use this data to explore complementary information. However, such real-world data are often incomplete. Existing algorithms for incomplete multi-view clustering (IMC) have some limitations, such as the ineffective use of valuable information hidden in the data, oversensitivity to model parameters, and ineffective handling of samples with incomplete views. To overcome these limitations, we present a novel algorithm for incomplete multi-view clustering using Non-negative matrix factorization and a low-rank tensor (IMC-NLT). In particular, IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.