The　rapid　development　of　digital　technology　is　reshaping　the　architecture　of　the　Industrial　Internet　of　Things　(IIoT).　The　traditional　architecture　can　not　process　vast　amounts　of　data　exchanges　and　provide　entities　with　trust.　The　future　IIoT　is　expected　to　be　a　decentralized　architecture　in　which　blockchain　and　digital　twin-driven　IIoT　can　enable　trusted　data　exchanges.　However,　this　architecture　can　not　obtain　huge　amounts　of　external　real-time　data　and　isolated　data.　Moreover,　it　can　not　handle　complex　industrial　computing　tasks.　Therefore,　we　combine　oracle　with　decentralized　learning　to　propose　a　novel　IIoT-oriented　digital　twin　architecture.　We　also　propose　an　effective　decentralized　collaboration　mechanism　to　support　external　data　and　resources　exchanges.　Moreover,　we　propose　a　novel　computing　collaboration　mechanism　to　expand　the　learning　capabilities　of　the　industrial　ecology.　Experiments　show　that　our　proposed　paradigm　has　less　processing　time,　a　more　stable　process,　and　better　learning　ability　compared　to　other　paradigms.　IEEE