  As a unique probe, precision measurement of \textit{pp} solar neutrinos is
important for studying the Sun's energy mechanism, monitoring thermodynamic
equilibrium, and studying neutrino oscillation in the vacuum-dominated region.
For a large-scale liquid scintillator detector, one bottleneck for \textit{pp}
solar neutrino detection comes from pile-up events of intrinsic $^{14}$C
decays. This paper presents a few approaches to discriminate \textit{pp} solar
neutrinos and $^{14}$C pile-up events by considering the difference in their
time and spatial distributions. In this work, a Geant4-based Monte Carlo
simulation is constructed. Then multivariate analysis and deep learning
technology were adopted respectively to investigate the capability of $^{14}$C
pile-up reduction. As a result, the BDTG model and VGG network showed good
performance in discriminating \textit{pp} solar neutrinos and $^{14}$C double
pile-up events. Their signal significance can achieve 10.3 and 15.6 using only
one day of statistics. In this case, the signal efficiency is 51.1\% for
discrimination using the BDTG model when rejecting 99.18\% $^{14}$C double
pile-up events, and the signal efficiency is 42.7\% for the case using the VGG
network when rejecting 99.81\% $^{14}$C double pile-up events.
