Dynamical　properties　of　proteins　play　an　essential　role　in　their　function　exertion.　The　elastic　network　model　(ENM)　is　an　effective　and　efficient　tool　in　characterizing　the　intrinsic　dynamical　properties　encoded　in　biomacromolecule　structures.　The　Gaussian　network　model　(GNM)　and　anisotropic　network　model　(ANM)　are　the　two　often-used　ENM　models.　Here,　we　introduce　an　equally　weighted　multiscale　ENM　(equally　weighted　mENM)　based　on　the　original　mENM　(denoted　as　mENM),　in　which　fitting　weights　of　Kirchhoff/Hessian　matrixes　in　mENM　are　removed　since　they　neglect　the　details　of　pairwise　interactions.　Then,　we　perform　its　comparison　with　the　mENM,　traditional　ENM,　and　parameter-free　ENM　(pfENM)　in　reproducing　dynamical　properties　for　the　six　representative　proteins　whose　molecular　dynamics　(MD)　trajectories　are　available　in　http://mmb.pcb.ub.es/MoDEL/.　In　the　results,　for　B-factor　prediction,　mENM　performs　best,　while　the　equally　weighted　mENM　performs　also　well,　better　than　the　traditional　ENM　and　pfENM　models.　As　to　the　dynamical　cross-correlation　map　calculation,　mENM　performs　worst,　while　the　results　produced　from　the　equally　weighted　mENM　and　pfENM　models　are　close　to　those　from　MD　trajectories　with　the　latter　a　little　better　than　the　former.　Furthermore,　encouragingly,　the　equally　weighted　mANM　displays　the　best　performance　in　capturing　the　functional　motional　modes,　followed　by　pfANM　and　traditional　ANM　models,　while　the　mANM　fails　in　all　the　cases.　This　work　is　helpful　for　strengthening　the　understanding　of　the　elastic　network　model　and　provides　a　valuable　guide　for　researchers　to　utilize　the　model　to　explore　protein　dynamics.　©　2021　American　Chemical　Society.