In modern chemical industries, significant economic losses and unnecessary energy consumption are constantly resulted from process failures. In order to eliminate them in time, the early identification of the root cause of abnormal process deviation is crucial. Generally, causal analysis based on process knowledge plays an important role in process fault isolation. But with the increasing process complexity, the root cause is difficult to obtain using knowledge-based method alone. As a result of the wide application of distributed control systems, a large number of process data have been collected, which makes data-driven methods for fault diagnosis an active field in recent years. In previous research, contribution plots are widely used in practice to find the variables that are major contributors to the fault. However, the propagation of contribution among variables makes the results fluctuate at different sample points. In this study, a novel fault diagnosis method based on information entropy and signed directed graph (SDG) is proposed. Information entropy is first applied to select significant variables to specific faults according to the distribution feature of variables. Then, the propagation path of selected nodes is identified by SDG model to diagnosis the root cause. Due to the exclusion of less correlated variables, the results at each sample are relatively consistent compared with contribution plots. In order to verify its effectiveness, the proposed method is applied to the benchmark Tennessee Eastman process. The propagation path of most process faults is identified, which is well matched with the fault description, indicating that the proposed method has a good performance on diagnosing process faults.