Dynamic　multiobjective　optimization　problems　(DMOPs)　vary　over　time,　requiring　an　optimization　algorithm　to　track　the　position　of　Pareto-optimal　front　(PF)　in　a　dynamic　environment.　To　achieve　that,　a　novel　co-evolutionary　algorithm　based　on　problem　analysis　(CAPA)　is　proposed　in　this　paper.　CAPA　is　designed　to　solve　DMOPs　from　decision　space　and　objective　space　simultaneously,　which　is　achieved　by　the　combination　of　adjustable　prediction　(AP)　and　precise　mapping　strategy　(PM).　In　decision　space,　the　proposed　multi-model　prediction　can　estimate　the　location　of　new　population　based　on　the　historical　median　points.　In　objective　space,　a　novel　sampling　method　is　developed　to　search　for　sample　points　with　better　convergence　or　diversity.　Then,　mapping　these　sample　points　back　to　decision　space　based　on　inverse　model.　Through　the　problem　analysis　mechanism,　the　proportion　of　the　new　solutions　produced　by　each　strategy　changes　adaptively.　CAPA　is　incorporated　into　the　dynamic　multiobjective　evolutionary　algorithm　(DMOEA)　based　on　decomposition　(MOEA/D)　to　construct　a　novel　algorithm.　The　efficacy　of　CAPA　is　validated　by　comparison　with　five　state-of-the-art　algorithms　on　28　benchmarks.　Experimental　results　show　that　CAPA　has　the　ability　to　generate　high　quality　population　uniformly　along　PF.