The problem of “alarm flooding” caused by the increase in the number of industrial alarm variables has seriously affected the proper functions of alarm systems. In response, this study proposes a method to learn the transfer relationship of alarm variables from the process alarm data set. First, leveraging the ability of transfer entropy to accurately measure the causal relationship between variables with first-order or multi-order autocorrelation, the study identifies the causal relationship between variables. Second, depending on the entropy value between variables, the nodes that transmit a large amount of information are reserved. Finally, the K2 algorithm is improved by considering the time proportions of variables in different states, and the final alarm propagation network is obtained by learning. The verification on the Tennessee Eastman process data set reveals that the method can judge the root cause of the alarm and well achieve alarm propagation paths through learning.