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As a unique probe, precision measurement of \textit{pp} solar neutrinos isimportant for studying the Sun's energy mechanism, monitoring thermodynamicequilibrium, 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 $^{14}$Cdecays. This paper presents a few approaches to discriminate \textit{pp} solarneutrinos and $^{14}$C pile-up events by considering the difference in theirtime and spatial distributions. In this work, a Geant4-based Monte Carlosimulation is constructed. Then multivariate analysis and deep learningtechnology were adopted respectively to investigate the capability of $^{14}$Cpile-up reduction. As a result, the BDTG model and VGG network showed goodperformance in discriminating \textit{pp} solar neutrinos and $^{14}$C doublepile-up events. Their signal significance can achieve 10.3 and 15.6 using onlyone day of statistics. In this case, the signal efficiency is 51.1\% fordiscrimination using the BDTG model when rejecting 99.18\% $^{14}$C doublepile-up events, and the signal efficiency is 42.7\% for the case using the VGGnetwork when rejecting 99.81\% $^{14}$C double pile-up events.

Discrimination of pp solar neutrinos and $^{14}$C double pile-up events in a large-scale LS detector