In　order　to　enable　more　and　more　multimedia　content　to　be　shared　in　the　vehicular　network,　edge　caching　is　a　promising　approach　to　cache　content　near　the　vehicles　to　reduce　the　burden　of　communication　link　and　improve　quality　of　service.　However,　the　high　mobility　of　vehicles　and　change　in　content　popularity　bring　new　challenges　to　edge　caching　in　dynamic　environment.　Under　the　limitation　of　cache　capacity,　we　propose　a　collaborative　caching　strategy　in　vehicular　network　to　maximize　the　data　throughput　obtained　from　edge　devices.　Specifically,　we　first　use　Hawkes　process　to　adapt　to　the　dynamic　change　of　contents＇　popularity.　Then,　a　cooperative　content　caching　scheme　based　on　deep　reinforcement　learning　(DRL)　is　proposed.　Finally,　the　performance　of　the　scheme　is　evaluated　by　simulation　experiments.