The　total　organic　carbon　(TOC)　is　a　key　geologic　parameter　for　unconventional　reservoirs.　Conventional　empirical　δ　Log　R　methods　cannot　handle　the　nonlinear　relationships　between　the　characteristics　of　TOC　and　its　well-log　responses.　Increased　data　availability　has　the　potential　to　speed　up　deep　learning　applications,　which　can　reasonably　propagate　the　integrated　information　from　well　logs　to　indirectly　observable　geologic　properties,　such　as　TOC.　Although　the　existing　convolutional　neural　network　(CNN)　has　found　superior　performance　to　δ　Log　R　for　predicting　TOC,　CNNs　feature-learning　capability　is　still　constrained　by　the　fact　that　it　can　only　extract　log-specific　sequential　features　of　the　input　logs.　However,　the　cross-log　topological　association　features　are　potentially　essential　for　the　nonlinear　mapping　between　well　logs　and　TOC.　Thus,　we　introduce　a　novel　deep　spatial-sequential　graph　convolutional　network　(SSGCN)　for　predicting　the　TOC　by　jointly　leveraging　the　cross-log　topological　association　features　and　log-specific　sequential　features.　Through　further　use　of　the　previously　unaccounted　topological　interactions,　our　SSGCN　dramatically　outperforms　the　sequence-based　CNN.　In　the　southeast　Sichuan　Basin,　SSGCN　exhibits　beneficial　mapping　not　demonstrated　previously:　its　models　achieve　a　better　cross-validation　performance　within　the　same　gas　field　wells　and　a　greater　generalizability　in　another　gas　field　well.　Our　SSGCN　method　can　predict　TOC　of　shale　gas　field　well　with　the　best　R2　being　0.87　within　1　s　on　the　CPU　of　a　desktop　computer,　which　increases　the　efficiency　of　obtaining　the　TOC　parameter.　From　this　study,　we　recommend　graph　and　sequential　convolutions　for　designing　deep　learning　architectures　in　the　well-log　analysis.　　©　2023　Society　of　Exploration　Geophysicists.