  Studies have shown that the use of pulsar timing arrays (PTAs) is among the
approaches with the highest potential to detect very low-frequency
gravitational waves in the near future. Although the capture of gravitational
waves (GWs) by PTAs has not been reported yet, many related theoretical studies
and some meaningful detection limits have been reported. In this study, we
focused on the nanohertz GWs from individual supermassive binary black holes.
Given specific pulsars (PSR J1909$-$3744, PSR J1713$+$0747, PSR J0437$-$4715),
the corresponding GW$-$induced timing residuals in PTAs with Gaussian white
noise can be simulated. Further, we report the classification of the simulated
PTA data and parameter estimation for potential GW sources using machine
learning based on neural networks. As a classifier, the convolutional neural
network shows high accuracy when the combined signal to noise ratio $\geq$1.33
for our simulated data. Further, we applied a recurrent neural network to
estimate the chirp mass ($\mathcal{M}$) of the source and luminosity distance
($\text{D}_{p}$) of the pulsars and Bayesian neural networks (BNNs) to obtain
the uncertainties of chirp mass estimation. Knowledge of the uncertainties is
crucial to astrophysical observation. In our case, the mean relative error of
chirp mass estimation is less than $13.6\%$. Although these results are
achieved for simulated PTA data, we believe that they will be important for
realizing intelligent processing in PTA data analysis.
