This study investigates the impact of nucleon-nucleon correlations on heavy-ion collisions using the hadronic transport model smash in $\sqrt{s_{NN}}=3$ GeV ${}^{197}\mathrm{Au}+{}^{197}\mathrm{Au}$ collisions. We developed an innovative Monte Carlo sampling method that incorporates both single-nucleon distributions and nucleon-nucleon correlations. By comparing three initial nuclear configurations—a standard Woods-Saxon distribution (un-corr), hard-sphere repulsion (step corr), and ab initio nucleon-nucleon correlations (nn-corr)—we revealed minimal differences in traditional observables except for ultracentral collisions. When distinguishing between un-corr and nn-corr configurations, conventional attention-based point-cloud networks and multievent mixing classifiers failed (accuracy 50%). To resolve this, we developed a novel deep learning architecture integrating multievent statistics and high-dimensional latent space feature correlations, achieving 60% overall classification accuracy, which improved to 70% for central collisions. This method enables the extraction of subtle nuclear structure signals through statistical analysis in high-dimensional latent space, offering a new paradigm for studying initial-state nuclear properties and quark-gluon plasma characteristics in heavy-ion collisions. It overcomes the limitations of traditional single-event analysis in detecting subtle initial-state differences.

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• Nucleon distribution
• Nucleon-nucleon interactions
• Particle correlations & fluctuations
• Transport in heavy-ion collisions
• Deep learning
• Machine learning