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After the fault occurs in the distribution network, the line current increases and the voltage drops rapidly, which affects the power quality and power supply reliability of the distribution network. In this study, the fault diagnosis system of the distribution network is established, and the fault location is quickly found and located by using the fault information collected. The transformer low-voltage side phase voltage waveform of two cycles before circuit breaker trip is collected, and the RMS value of fundamental wave is calculated by Fourier plus window transform, and the variation rule of low-voltage side phase voltage after failure is analyzed. The system uses a batch matching algorithm based on multi-semantic interaction and encoders are used to encode fault text information. Experimental results show that the training speed of the fault diagnosis model of the system in this study is fast, and the fault diagnosis accuracy is up to 100%. In the language push task test, the accuracy of SNL data set is 100%.