Script Event Prediction refers to predicting the subsequent event based on a given existing chain of context events. In the real world， the relationship of different events can be naturally represented as a graph structure， where events serve as nodes and their temporal or causal relations are depicted as edges. However， previous approaches that automatically constructed event graphs suffer from sparsity problem due to the limited scale of corpus and the incapability of information extraction tools. Moreover， they fail to integrate information from higher order nodes to support multi-step reasoning. To remedy this， we propose a Dense Event Graph （DEG） approach which use a learnable multi-dimensional weighted adjacency matrix to address the sparsity issue and characterize the relation strengths between events. To embed the DEG， we propose a general framework capable of combining high-order event evolution information into the event representations. Experimental results on the multiple choice narrative cloze （MCNC） and coherent multiple choice narrative cloze （CMCNC） demonstrate the effectiveness of our approach.