The　manipulator　must　have　a　humanoid　structure　and　human-like　motion　to　achieve　close　and　effective　collaboration　with　humans.　The　anthropo-morphic　manipulator　has　a　structure　similar　to　a　human　arm.　We　propose　a　human-like　motion　plan-ning　method　for　moving　target　scenarios.　We　use　dynamic　movement　primitives　(DMPs)　to　model　the　anthropomorphic　manipulator＇s　end-effector　motion.　Compared　to　the　conventional　DMPs　formalism,　the　proposed　method　contains　the　following　extensions:1)　an　orientation-based　spatial　scaling,2)　online　temporal　scaling　parameter　adaptation　with　moving　targets　feed-back.　And　we　use　a　deep　residual　convolution　neural　network　(ResCNN)　to　model　the　elbow　motion　of　the　anthropomorphic　manipulator.　Based　on　these　models,　real-time　trajectory　planning　is　performed　for　the　moving　target.　In　real-time　motion　planning,　there　is　no　need　to　model　the　moving　target;　only　the　moving　tar-get＇s　current　position　and　velocity　information　is　used　to　obtain　a　human-like　motion　of　the　anthropomorphic　manipulator　regarding　posture　and　velocity.　Simulation　experiments　on　the　Kinova　Jaco2　in　Coppeliasim　show　that　our　approach　is　practical.　©　2023　IEEE.