In order to effectively extract the characteristics of electric shock faults and realize the separation of electric shock current from residual current，this paper proposes a new method of electric shock current extraction based on the combination of Variational Mode Decomposition（VMD）and Long Short Term Memory（LSTM）. The parameters of VMD［K，α］were optimized by fruit fly optimization algorithm to obtain the optimal combination of pa? rameters［6，280］. Based on the mutation characteristics of the optimal modal component of the residual current de? composed by VMD，the growth rates η1 and η2 of the sum of the current amplitudes in adjacent periods are defined as the characteristic quantities for judging electric shock accident. The electric shock current signal is reconstructed from the six layers modal component signal，and the electric shock current detection model based on LSTM network is constructed. The results of 240 groups of electric shock signals show that，when at least one of η1 and η2 is greater than 1%，electric shock occurs，otherwise no electric shock accident occurs. Compared with the VMD-BP and VMD-RBF detection models，the average correlation coefficients of the VMD-LSTM detection model are increased by 6.2% and 2.3%，respectively，and the average root mean square error is reduced by 36.8% and 27.1%，respectively. The method proposed in this paper has higher detection accuracy. The research results provide a certain reference for the development of residual current protection devices based on the electric shock current of the biological body.