Multivariate time series (MTS) forecasting is an important problem in many fields. Accurate forecasting results can effectively help decision-making. To date, many MTS forecasting methods have been proposed and widely applied. However, these methods assume that the predicted value of a single variable is affected by all other variables, which ignores the causal relationship among variables. To address the above issue, a novel end-to-end deep learning model, termed graph neural network with transfer entropy (TEGNN) is proposed in this paper. To characterize the causal information among variables, the transfer entropy (TE) graph is introduced in our model, where each variable is regarded as a graph node and each edge represents the casual relationship between variables. In addition, convolutional neural network (CNN) filters with different perception scales are used for time series feature extraction, which is used to generate the feature of each node. Finally, graph neural network (GNN) is adopted to tackle the forecasting problem of graph structure generated by MTS. Three benchmark datasets from the real world are used to evaluate the proposed TEGNN and the comprehensive experiments show that the proposed method achieves state-of-the-art results in MTS forecasting task.