The　ever-increasing　and　unbalanced　traffic　load　in　cellular　vehicle-to-everything　(C-V2X)　networks　have　increased　the　network　congestion　and　led　to　user　dissatisfaction.　To　relieve　the　network　congestion　and　improve　the　traffic　load　balance,　in　this　paper,　we　propose　an　intelligent　software　defined　C-V2X　network　framework　to　enable　flexible　and　low-complexity　traffic　offloading　by　decoupling　the　network　data　plane　from　the　control　plane.　In　the　data　plane,　the　cellular　traffic　offloading　and　the　vehicle　assisted　traffic　offloading　are　jointly　performed.　In　the　control　plane,　deep　learning　is　deployed　to　reduce　the　software　defined　network　(SDN)　control　complexity　and　improve　the　traffic　offloading　efficiency.　Under　the　proposed　framework,　we　investigate　the　traffic　offloading　problem,　which　can　be　formulated　as　a　multi-objective　optimization　problem.　Specifically,　the　first　objective　maximizes　the　cellular　access　point　(AP)　throughput　with　consideration　of　the　load　balance　by　associating　the　users　with　the　APs.　The　second　objective　maximizes　the　vehicle　throughput　with　consideration　of　the　vehicle　trajectory　by　associating　the　delay-insensitive　users　with　the　vehicles.　The　two　objectives　are　coupled　by　the　association　between　the　cellular　APs　and　the　vehicles.　A　deep　learning　based　online-offline　approach　is　proposed　to　solve　the　multi-objective　optimization　problem.　The　online　stage　decouples　the　optimization　problem　into　two　sub-problems　and　utilizes　the　＇Pareto　optimal＇　to　find　the　solutions.　The　offline　stage　utilizes　deep　learning　to　learn　from　the　historical　optimization　information　of　the　online　stage　and　helps　predict　the　optimal　solutions　with　reduced　complexity.　Numerical　results　are　provided　to　validate　the　advantages　of　our　proposed　traffic　offloading　approach　via　deep　learning　in　C-V2X　networks.