The accumulation of intermittent faults will cause the deterioration of the health of the electronic system. Correctly identifying the severity of intermittent faults can ensure the safe operation and reduce maintenance costs of the electrical systems. However, it is difficult to extract intermittent fault features accurately, which leads to the failure of traditional identification methods. This paper proposes a method for identifying the severity of intermittent faults based on LSTM network. First, the intermittent faults are injected into the electronic system to obtain sufficient training data of different severity. Then use these data to train the classifier which is constructed by LSTM network and the softmax fully connected layer network. Finally, by injecting faults into typical circuits and using the trained LSTM network to identify the severity of intermittent faults, the experimental results prove the effectiveness and feasibility of the method.