Redundant　manipulators　(RMs)　have　been　gaining　more　attention　thanks　to　their　excellent　merits　of　operating　flexibility　and　precision.　Inverse　kinematics　(IK)　study　is　critical　to　the　design,　trajectory　planning,　and　control　of　RMs,　while　it　is　usually　more　complicated　to　solve　IK　problems　which　may　inherently　have　innumerable　solutions.　In　this　work,　a　novel　approach　for　solving　the　IK　problems　for　RMs　while　retaining　the　redundancy　characteristics　has　been　proposed.　By　employing　a　constraint　function,　the　method　delicately　reduces　the　infinite　IK　solutions　of　a　RM　to　a　finite　set.　Furthermore,　the　workspace　of　RMs　is　divided　into　nonlinear　partitions　through　diverse　joint　angle　intervals,　which　have　further　simplified　the　mapping　correlations　between　the　desired　point　and　manipulators＇　joint　angles.　For　each　partition,　a　pre-trained　neural　network　(NN)　model　is　established　to　acquire　its　IK　solutions　with　high　efficiency　and　precision.　After　combing　all　nonlinear　partitions,　multiple　reasonable　IK　solutions　are　available.　The　presented　method　offers　a　possible　selection　of　the　most　appropriate　solution　for　trajectory　planning　and　energy　consumption　and　therefore　has　the　potential　for　facilitating　novel　robot　development.