In　this　study,　SEM　observation　showed　that　in　four　different　CB/NRs,　as　the　amount　of　filled　CB　increased,　CB　agglomeration　became　more　serious,　and　the　CB　shape　wasn＇t　a　simple　sphere.　Combining　FEA　with　machine　learning,　the　study　optimized　CB　particle　structure　parameters,　shape,　and　the　aspect　ratio　of　the　ellipse.　Using　these　optimized　parameters,　it　established　a　3D　stochastic　model　without　aggregates　and　a　3D　model　with　aggregates　to　study　the　uniaxial　tensile　mechanical　behavior　of　CB/NRs,　adopting　the　Mooney–Rivlin　phenomenological　model.　The　results　indicated　that　the　ellipsoidal　particle　model　was　slightly　better　than　the　spherical　one　in　predicting　the　mechanical　behavior　of　CB/NRs.　Specifically,　the　Random　Forest　algorithm　fitting,　cross-validation,　and　grid　search　for　hyperparameters　to　obtain　the　minimum　RMSE　had　high　prediction　accuracy　and　fitting　effectiveness.　The　optimal　aspect　ratio　range　was　determined　to　be　2.2–2.4.　Moreover,　compared　with　experimental　results,　the　RVE　model　with　aggregates　described　the　constitutive　behavior　of　CB/NRs　more　accurately,　better　addressed　the　large　deviations　between　FE　simulation　and　experimental　curves　at　high　CB　volume　fractions　in　CB/NRs,　and　provided　modeling　solutions　for　CB/NRs.　©　2025　Society　of　Plastics　Engineers.