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.