With　the　continuous　addition　of　an　abundant　of　heterogeneous　devices,　the　limitation　of　task　delay　has　become　an　obstacle　to　the　development　of　the　Industrial　Internet　of　Things　(IIoT).　Task　offloading　based　on　edge　computing　can　provide　low-latency　computing　services　for　these　tasks.　However,　in　the　actual　IIoT　scenario,　in　contrast　to　cloud　computing,　edge　computing　has　limited　resources　and　computing　capabilities.　Resource-constrained　edge　resources　cannot　meet　the　offloading　requirements　of　massive　industrial　devices.　In　this　article,　we　propose　an　optimal　joint　offloading　scheme　based　on　resource　occupancy　prediction　for　the　problem　of　computing　offloading　with　limited　edge　resources.　The　scheme　is　divided　into　two　parts,　including　edge　resource　occupancy　prediction　and　task　offloading.　Simultaneously,　considering　multitask　and　the　limitations　of　edge　resources,　gate　recurrent　unit　(GRU)　is　used　to　predict　the　occupancy　of　edge　resources.　Formulating　an　optimal　strategy　of　task　offloading　by　using　a　reinforcement　learning　algorithm　according　to　the　network　state　and　predicted　results.　The　simulation　results　show　that　the　scheme　can　effectively　reduce　the　average　delay　of　tasks,　while　minimizing　the　task　offloading　failure　rate.