Aiming at the problem of low accuracy and slow training speed in complex circuits with multiple electrical faults, a method of window division combined with wavelet decomposition and empirical mode decomposition ( EMD) is proposed to extract current characteristic quantities respectively from multiple dimensions in time domain, frequency domain and time scale, identifying arc fault by using machine learning classification models. Firstly, the fault and normal current data are collected by the electrical fault experimental platform, and the current data is segmented by window. Then, the wavelet transforming and EMD methods are used to decompose the current signal and calculate the characteristic quantities in different dimensions. The characteristic information collected is used as the input of the classification algorithm for arc fault diagnosis. The experimental results show that the arc fault detection accuracy of the feature extraction method on the gradient boosting decision tree (GBDT) is as high as 98%, which is 1. 87% higher than that of the current without segmentation. It can effectively obtain the arc fault characteristics and realize the detection of arc fault with high efficiency and high accuracy.