Complex　data-parallel　job　contains　task　dependency　information　defined　as　Directed　Acyclic　Graph　(DAG).　For　convenience,　the　DAG　presented　data-parallel　jobs　are　named　as　DAG　jobs.　The　prevalence　of　DAG　jobs　in　modern　data　centers　has　made　the　scheduling　oriented　job　characterization　a　big　challenge.　This　paper　proposes　a　deep　graph-temporal　clustering　framework,　i.e.,　DeGTeC,　to　efficiently　categorize　DAG　jobs　leveraging　the　graphic　and　temporal　information　in　DAGs.　The　categorization　result　can　then　be　naturally　used　to　characterize　the　resource　consumption　pattern　of　DAG　jobs.　The　DeGTeC　framework　is　constructed　mainly　based　on　two　autoencoders,　i.e.,　TaskAE　and　JobAE.　TaskAE　and　JobAE　contain　spectral　graph　convolutional　network　(GCN)　layers,　temporal　convolutional　network　(TCN)　layers,　and　the　adaptive　pooling　layers　to　help　build　task　embeddings　and　job　embeddings.　An　extra　embedding　sorting　step　takes　in　the　sequential　order　information　and　the　depth-bias　information　for　job　clustering.　To　our　best　knowledge,　DeGTeC　is　the　first　solution　to　do　resource　consumption　characterization　of　DAG　jobs　fully　leveraging　the　task　dependencies　defined　in　DAG.　Experimental　results　demonstrate　that　the　DeGTeC　framework　outperforms　the　state-of-the-art　job　resource　consumption　characterization　methods.　©　2022　Elsevier　B.V.