Distributed　learning-based　high-dimensional　temporal　modeling　for　the　Industrial　Internet　of　Things　(IIoT)　has　become　a　prevailing　trend.　However,　traditional　distributed　learning　inefficiently　extracts　information　by　straightforward　architects,　resulting　in　low　modeling　accuracy　and　high　communication　costs.　We　propose　a　distributed　hierarchical　temporal　graph　learning　(DHTGL)　approach.　In　terminal　equipment,　we　construct　an　adaptive　hierarchical　dilation　convolutional　network　to　dynamically　capture　spatiotemporal　features　by　adjusting　the　dilation　factor　at　each　layer.　Next,　we　construct　adaptive　graphs　according　to　the　connection　similarity　between　dimensions　to　capture　implicit　connections.　In　the　edge　device,　we　design　a　node-edge　graph　distance　calculation　based　on　Gromov-Wasserstein　distance　to　group　feature　graphs　and　construct　representative　cluster　feature　graphs.　Edge　devices　upload　cluster　feature　graphs　to　reduce　communication　costs　while　minimizing　information　loss.　In　the　central　server,　we　incorporate　graph　attention　networks　into　graph　neural　networks　for　edge　updating　in　training　models　on　clustered　feature　graphs.　Experiments　using　public　IIoT　datasets　and　the　self-built　IIoT　platform　demonstrate　the　effectiveness　of　DHTGL　in　comparison　with　common　distributed　learning　approaches.　The　results　confirm　that　DHTGL　consumes　fewer　communications　while　achieving　higher　accuracies.　IEEE