Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable clustering effectiveness and well-defined framework. Despite this, due to its high computation complexity, it is unable of handling large-scale or high-dimensional data, particularly multi-view large-scale data. To address this issue, in this paper, we propose a fast multi-view clustering algorithm with spectral embedding (FMCSE), which speeds up both the spectral embedding and spectral analysis stages of multi-view spectral clustering. Furthermore, unlike conventional spectral clustering, FMCSE can acquire all sample categories directly after optimization without extra k-means, which can significantly enhance efficiency. Moreover, we also provide a fast optimization strategy for solving the FMCSE model, which divides the optimization problem into three decoupled small-scale sub-problems that can be solved in a few iteration steps. Finally, extensive experiments on a variety of real-world datasets (including large-scale and high-dimensional datasets) show that, when compared to other state-of-the-art fast multi-view clustering baselines, FMCSE can maintain comparable or even better clustering effectiveness while significantly improving clustering efficiency.