The objective of simulating electron identification for the sPHENIX experiment is to accurately identify electrons from quarkonium (J/$\psi$, $\Upsilon$, etc.) bi-electron decay while effectively suppressing hadron background noise. By minimizing background, the signal-to-noise ratio is improved, which is essential for investigating the yield suppression effect of $\Upsilon$ particles within Quark-Gluon Plasma environments. This note primarily relies on Monte Carlo simulations of the sPHENIX detector, comparing conventional cut-based methods with machine learning approaches utilizing Multiple Variable Analysis for electron identification. The latter method notably enhances the capabilities of electron identification, offering improved accuracy and efficiency in identifying electrons from the background.