  We use the deep learning algorithm to learn the Reissner-Nordstr\"om(RN)
black hole metric by building a deep neural network. Plenty of data is made in
boundary of AdS and we propagate it to the black hole horizon through AdS
metric and equation of motion(e.o.m). We label this data according to the
values near the horizon, and together with initial data constitute a data set.
Then we construct corresponding deep neural network and train it with the data
set to obtain the Reissner-Nordstrom(RN) black hole metric. Finally, we discuss
the effects of learning rate, batch-size and initialization on the training
process.
