Mobile　Edge　Computing　(MEC)　offers　low-latency　and　high-bandwidth　support　for　Internet-of-Vehicles　(IoV)　applications.　However,　due　to　high　vehicle　mobility　and　finite　communication　coverage　of　base　stations,　it　is　hard　to　maintain　uninterrupted　and　high-quality　services　without　proper　service　migration　among　MEC　servers.　Existing　solutions　commonly　rely　on　prior　knowledge　and　rarely　consider　efficient　resource　allocation　during　the　service　migration　process,　making　it　hard　to　reach　optimal　performance　in　dynamic　IoV　environments.　To　address　these　important　challenges,　we　propose　SR-CL,　a　novel　mobility-aware　seamless　Service　migration　and　Resource　allocation　framework　via　Convex-optimization-enabled　deep　reinforcement　Learning　in　multi-edge　IoV　systems.　First,　we　decouple　the　Mixed　Integer　Nonlinear　Programming　(MINLP)　problem　of　service　migration　and　resource　allocation　into　two　sub-problems.　Next,　we　design　a　new　actor-critic-based　asynchronous-update　deep　reinforcement　learning　method　to　handle　service　migration,　where　the　delayed-update　actor　makes　migration　decisions　and　the　one-step-update　critic　evaluates　the　decisions　to　guide　the　policy　update.　Notably,　we　theoretically　derive　the　optimal　resource　allocation　with　convex　optimization　for　each　MEC　server,　thereby　further　improving　system　performance.　Using　the　real-world　datasets　of　vehicle　trajectories　and　testbed,　extensive　experiments　are　conducted　to　verify　the　effectiveness　of　the　proposed　SR-CL.　Compared　to　benchmark　methods,　the　SR-CL　achieves　superior　convergence　and　delay　performance　under　various　scenarios.