In recent years, multiview learning technologies have attracted a surge of interest in the machine learning domain. However, when facing complex and diverse applications, most multiview learning methods mainly focus on specific fields rather than provide a scalable and robust proposal for different tasks. Moreover, most conventional methods used in these tasks are based on single view, which cannot be readily extended into the multiview scenario. Therefore, how to provide an efficient and scalable multiview framework is very necessary yet full of challenges. Inspired by the fact that most of the existing single view algorithms are graph-based ones to learn the complex structures within given data, this article aims at leveraging most existing graph embedding works into one formula via introducing the graph consensus term and proposes a unified and scalable multiview learning framework, termed graph consensus multiview framework (GCMF). GCMF attempts to make full advantage of graph-based works and rich information in the multiview data at the same time. On one hand, the proposed method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods; on the other hand, learned graphs can be flexibly chosen to construct the graph consensus term, which can more stably explore the correlations among multiple views. To this end, GCMF can simultaneously take the diversity and complementary information among different views into consideration. To further facilitate related research, we provide an implementation of the multiview extension for locality linear embedding (LLE), named GCMF-LLE, which can be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.