As　an　important　application　scenario　of　the　industrial　Internet　of　things,　the　Internet　of　Vehicles　can　significantly　improve　road　safety,　improve　traffic　management　efficiency,　and　improve　people＇s　travel　experience.　Due　to　the　high　dynamics　of　the　Internet　of　vehicles　environment,　the　traditional　resource　optimization　technologies　cannot　meet　the　requirements　of　the　Internet　of　vehicles　for　dynamic　communication,　computing　and　storage　resources　optimization　management,　and　artificial　intelligence　algorithms　can　adaptively　obtain　dynamic　resource　allocation　schemes　through　self-learning.　Therefore,　adopting　artificial　intelligence　techniques　to　optimize　the　dynamic　resource　of　the　Internet　of　Vehicles　is　the　research　focus　of　this　article.　In　this　article,　we　first　model　the　Internet　of　Vehicles　resource　allocation　problem　as　a　semi-Markov　decision　process　that　introduces　a　resource　reservation　strategy　and　a　secondary　resource　allocation　mechanism.　Then,　the　reinforcement　learning　algorithm　is　used　to　solve　the　model.　Thereafter,　it　theoretically　analyzes　the　joint　optimization　of　computing　and　communication　resources,　models　it　as　a　hierarchical　architecture,　and　uses　hierarchical　reinforcement　learning　to　obtain　the　optimal　system　resource　allocation　plan.　Finally,　the　results　of　simulation　experiments　show　that　the　dynamic　resource　allocation　scheme　of　the　Internet　of　vehicles　based　on　the　reinforcement　learning　in　this　article　greatly　improve　resource　utilization　and　user　quality　of　experience　with　guaranteeing　system　quality　of　service　compared　with　the　traditional　greedy　algorithm.