The timely and efficient processing of alarm information in the power backbone communication network can provide an opportunity for fault handling. By applying artificial intelligence technology based on historical data

the occurrence of alarms can be predicted. A complete data set is the basic condition for successful prediction. The data collected in the real network often has problems such as uneven distribution and missing data. How to construct a complete data set is a key issue based on artificial intelligence technology. This paper proposes a data preprocessing method for alarm prediction in power backbone communication network. Data enhancement technology is used to complete the class balance of data

so that the final data set conforms to the data size input by the model. The simulation results show that the data pre-processing method based on data enhancement can improve the accuracy of alarm prediction to a large extent.