Dynamic　multi-objective　optimization　problems　(DMOPs)　are　mainly　reflected　in　objective　changes　with　changes　in　the　environment.　To　solve　DMOPs,　a　transfer　learning　(TL)　approach　is　used,　which　can　continuously　adapt　to　environmental　changes　and　reuse　valuable　knowledge　from　the　past.　However,　if　all　individuals　are　transferred,　they　may　experience　negative　transfers.　Therefore,　this　paper　proposes　a　novel　knowledge　transfer　method　for　the　dynamic　multi-objective　evolutionary　algorithm　(T-DMOEA)　to　solve　DMOPs,　which　consists　of　a　multi-time　prediction　model　(MTPM)　and　a　manifold　TL　algorithm.　First,　according　to　the　movement　trend　of　historical　knee　points,　the　MTPM　model　uses　a　weighted　method　to　effectively　track　knee　points　after　environmental　changes.　Then,　the　knowledge　of　the　suboptimal　solution　is　reused　in　the　non-knee　point　set　using　the　manifold　TL　technique,　which　yields　more　high-quality　individuals　and　speeds　up　the　convergence.　In　the　dynamic　evolutionary　process,　the　knee　points　and　high-quality　solutions　are　combined　to　guide　the　generation　of　the　initial　population　in　the　next　environment,　ensuring　the　diversity　of　the　population　while　reducing　the　computational　cost.　The　experimental　results　show　that　the　proposed　T-DMOEA　algorithm　can　converge　rapidly　in　solving　DMOPs　while　obtaining　better-quality　solutions.　©　2023