Vehicular　ad　hoc　networks　(VANETs)　have　become　a　promising　technology　in　intelligent　transportation　systems　(ITS)　with　rising　interest　of　expedient,　safe,　and　high-efficient　transportation.　VANETs　are　vulnerable　to　malicious　nodes　and　result　in　performance　degradation　because　of　dynamicity　and　infrastructure-less.　In　this　paper,　we　propose　a　trust　based　dueling　deep　reinforcement　learning　approach　(T-DDRL)　for　communication　of　connected　vehicles,　we　deploy　a　dueling　network　architecture　into　a　logically　centralized　controller　of　software-defined　networking　(SDN).　Specifically,　the　SDN　controller　is　used　as　an　agent　to　learn　the　most　trusted　routing　path　by　deep　neural　network　(DNN)　in　VANETs,　where　the　trust　model　is　designed　to　evaluate　neighbors＇　behaviour　of　forwarding　routing　information.　Simulation　results　are　presented　to　show　the　effectiveness　of　the　proposed　T-DDRL　framework.