By exploring complex graph information hidden in data from multiple views, multi-view clustering based on graph neural network significantly enhances the clustering performance and has drawn increasing attention in recent years. Although considerable progress has been made, most existing GNN based MVC models merely consider the explicit presence of graph structure in raw data and ignore that latent graphs of different views also provide specific information for the clustering task. We propose dynamic weighted graph fusion for deep multi-view clustering (DFMVC) to address this issue. Specifically, DFMVC learns embedded features via deep autoencoders and then constructs latent graphs for each individual view. Then, it concatenates the embedded features of all views to form a global feature to leverage complementary information, as well as generates a fusion graph via combining all latent graphs to accurately capture the topological information among samples. Based on the informative fusion graph and global features, the graph convolution module is adopted to derive a representation with global comprehensive information, which is further used to generate pseudo-label information. In a self-supervised manner, such information guides each view to dynamically learn discriminative features and latent graphs. Extensive experimental results demonstrate the efficacy of DFMVC.