In the process of aircraft maintenance, the aviation maintenance company has accumulated a large number of empirical maintenance log data. Machine learning methods can be used to help maintenance staff to make correct fault diagnosis decisions, using this type of maintenance log reasonably. Firstly, according to the particularity of the maintenance log, an iterative fault diagnosis process is proposed. Secondly, based on the traditional text feature extraction technology, the text feature extraction method based on convolution neural network (CNN) with the information in the domain is proposed, which is used in the case of small sample size. The method uses the target vector to train word vector to get more adequate text features. Finally, the random forest (RF) model is used in combination with other fault characteristics to determine the cause of aircraft equipment failure. The convolutional neural network aims at the cause of the failure, and pre-trains the word vector in the fault phenomenon to obtain a text feature that better reflects the field. Compared with other text feature extraction methods, the method obtains better results in the case of small sample size. At the same time, the convolutional neural network and random forest model are applied to the identification of aircraft equipment failure, and compared with other text feature extraction methods and machine learning prediction models, which illustrates the rationality and necessity of the method of text feature extraction and the method of fault cause identification.