Graph clustering has become an important research topic due to the proliferation of graph data. However, existing methods suffer from two major drawbacks. On the one hand, most methods can not simultaneously exploit attribute and graph structure information. On the other hand, most methods are incapable of handling multi-view data which contain sets of different features and graphs. In this paper, we propose a novel Multi-view Attributed Graph Clustering (MvAGC) method, which is simple yet effective. Firstly, a graph filter is applied to features to obtain a smooth representation without the need of learning the parameters of neural networks. Secondly, a novel strategy is designed to select a few anchor points, so as to reduce the computation complexity. Thirdly, a new regularizer is developed to explore high-order neighborhood information. Our extensive experiments indicate that our method works surprisingly well with respect to state-of-the-art deep neural network methods. The source code is available at https://github.com/sckangz/MvAGC.