Four-wheel-drive　battery　electric　vehicles　(BEV)　driven　by　multiple　motors　on　different　axles　are　getting　popular　by　offering　outstanding　dynamic　and　safety　performance　without　sacrificing　structure　complexity.　However,　efficiently　splitting　the　power　flow　between　power　sources　is　crucial　and　difficult.　In　this　study,　an　intelligent　energy　management　strategy　(EMS)　is　proposed　for　a　specific　dual-motor　four-wheel-drive　(DM-4WD)　BEV　to　reduce　energy　consumption　in　unknown　traffic　conditions.　A　novel　reward　factor　involved　deep　deterministic　policy　gradient　(DDPG)　algorithm　is　proposed　in　EMS　design,　whose　parameters　matching　are　based　on　particle　swarm　optimization　algorithm　to　provide　a　platform　to　investigate　the　maximum　potential　of　energy　performance　improvement　for　the　proposed　EMS.　The　simulation　results　show　that　the　proposed　DDPG-EMS　reaches　95.7%,　94.8%,　and　95.5%　of　benchmark　dynamic　programming-EMS　energy　performance　and　outperforms　the　discontinued-action-based　double　deep　Q-learning　strategy　in　unknow　driving　cycles.　Furthermore,　the　adaptability　of　DDPG-EMS　is　improved　by　introducing　novel　rewards　setting,　which　is　3%,　3.8%,　and　2.4%　better　than　the　traditional　State-of-Charge　(SOC)-based　DDPG-EMS.　The　simulation　results　suggest　the　proposed　strategy　is　efficient　and　instructive　for　multi-power　BEV　EMS　design.