Recently,　Internet　of　Vehicles　(IoV)　and　Machine　Learning　(ML)　have　attracted　more　and　more　attention.　Considering　inefficient　real-time　training　and　high　requirements　on　computing　capabilities　of　centralized　data　collection,　performing　Distributed　Machine　Learning　(DML)　in　IoV　has　become　an　important　research　branch.　However,　the　heterogeneity,　mobility,　and　distrust　among　IoV　nodes　affect　how　to　execute　DML　effectively,　securely,　and　in　a　salable　manner.　In　this　paper,　a　blockchain-based　Cooperative　Learning　framework　combined　with　a　Deep　Compression　method　(CLDC)　is　proposed.　First,　we　improve　the　local　training　efficiency　of　lightweight　IoV　nodes　by　using　deep　compression　method.　Meanwhile,　we　have　introduced　a　blockchain　system　in　CLDC,　the　significance　of　which　is　that　we　have　completed　the　transformation　from　centralized　architecture　to　distributed　framework　through　the　blockchain,　and　shared　local　training　results　in　a　verifiable　manner.　The　framework　uses　non-tamperable　features　of　the　blockchain　to　ensure　the　security　of　local　training　results.　Moreover,　we　propose　a　Learning-based　Redundant　Byzantine　Fault　Tolerance　(L-RBFT)　protocol,　in　which　the　primary　node　needs　to　confirm　the　loss　percentage　of　learning　in　the　transaction　before　forwarding　the　RBFT　messages.　The　significance　of　L-RBFT　is　to　ensure　that　IoV　nodes　obtain　the　best　training　results　through　the　consensus　of　blockchain　nodes.　We　use　it　to　solve　the　computing　and　communication　resource　allocation　problem　in　IoV　to　clarify　the　operating　mechanism　of　the　proposed　framework.　The　experimental　results　prove　that　this　scheme　performs　better　when　compared　with　the　traditional　centralized　deep　reinforcement　learning　method.