Because　of　insufficient　data,　cross-class　transfer　learning　has　potential　prospects　in　motor　imagery　electroencephalogram　(MI-EEG)　based　rehabilitation　engineering,　and　it　has　not　been　effectively　addressed　to　reduce　cross-class　variability　between　source　and　target　domains　and　find　the　best　class-to-class　transitive　correspondence　(CCTC).　In　this　paper,　we　propose　an　adaptive　cross-class　transfer　learning　(ACTL)　framework　with　twolevel　alignment　(TLA)　for　MI　decoding.　By　using　the　fast　partitioning　around　medoids　method,　the　central　samples　of　each　class　are　acquired　from　the　source　domain　and　target　domain　respectively.　Then,　they　are　applied　to　construct　the　central　alignment　matrix　to　realize　the　1st-level　domain　alignment　for　any　possible　CCTC　case.　The　2nd-level　subject　alignment　is　performed　by　Euclidean　alignment　for　each　class　of　aligned　source　domain.　Furthermore,　the　central　samples　of　the　target　domain　together　with　the　two-level　aligned　source　domain　are　mapped　into　tangent　space,　the　resulting　features　are　fed　to　a　teacher　convolutional　neural　network　(TCNN),　it　and　the　transferred　student　CNN　(SCNN)　are　trained　successively,　and　their　parameters　of　distillation　loss　are　optimized　automatically　by　a　scaling-based　grid　search　method.　Experiments　are　conducted　on　a　public　MI-EEG　dataset　with　multiple　subjects　and　MI-tasks,　the　proposed　framework　can　find　the　optimal　SCNN　associated　with　the　best　CCTC,　which　achieves　a　statistically　significant　average　classification　accuracy　of　86.37%.　The　results　suggest　that　TLA　is　helpful　for　increasing　the　distribution　similarity　between　different　domains,　and　the　knowledge　distillation　embedded　in　ACTL　framework　greatly　simplifies　the　SCNN　and　outperforms　the　complicated　TCNN.