Abstract:  To realize the estimation of high-energy electron flux and the risk assessment of spacecraft deep charging and discharging，an artificial neural network （ANN）for the electron flux inversion with deep charging was built based on the relation between deep charging and electron flux.The detect currents of a deep charging detector and the electron energy were taken as the model inputs，while the electron fluxes were taken as the output.AE9 was used to train the network，and the MSE of this model was reduced to 0.04122.The deep charging data from Giove-A and electron flux from GOES were used to verify the model′s accuracy.Based on this model，another ANN model was built to calculate the maximum internal electric field of the typical dielectric of spacecraft from the detection current to realize the real-time assessment of charging risk in spacecraft.

Key words: deep charging, neural network, inversion model, electronic environment, risk assessment