Background　Prognostic　models　for　esophageal　cancer　based　on　contrast-enhanced　chest　CT　can　aid　thoracic　surgeons　in　developing　personalized　treatment　plans　to　optimize　patient　outcomes.　However,　the　extensive　lymphatic　drainage　and　early　lymph　node　metastasis　of　the　esophagus　present　significant　challenges　in　extracting　and　analyzing　meaningful　lymph　node　characteristics.　Previous　studies　have　primarily　focused　on　tumor　and　lymph　node　features　separately,　overlooking　spatial　correlations　such　as　position,　direction,　and　volumetric　ratio.Methods　A　total　of　285　patients　who　underwent　radical　resection　surgery　at　Fujian　Provincial　Hospital　from　2018　to　2022　were　retrospectively　analyzed.　This　study　introduced　a　tumor-lymph　node　projection　plane,　created　by　projecting　lymph　node　ROIs　onto　the　tumor　ROI　plane.　A　ResNet-CBAM　model,　integrating　a　residual　convolutional　neural　network　with　a　CBAM　attention　module,　was　employed　for　feature　extraction　and　survival　prediction.　The　PJ　group　utilized　tumor-lymph　node　projection　planes　as　training　data,　while　the　TM　and　ZC　groups　utilized　tumor　ROIs　and　concatenated　images　of　tumor　and　lymph　node　ROIs,　respectively,　as　controls.　Additional　comparisons　were　made　with　traditional　machine　learning　models　(support　vector　machines,　logistic　regression,　and　K-nearest　neighbors).　Survival　outcomes　(median,　1-year,　3-year,　5-year)　were　used　as　target　labels　to　evaluate　model　performance　in　distinguishing　high-risk　patients　and　predicting　both　short-　and　long-term　survival.Results　In　the　PJ　group,　the　ResNet-CBAM　model　achieved　accuracy　rates　of　0.766,　0.981,　0.883,　and　0.778　for　predicting　median,　1-year,　3-year,　and　5-year　survival,　respectively.　Its　corresponding　AUC　values　for　1-,　3-,　and　5-year　survival　were　0.992,　0.913,　and　0.835.　Kaplan-Meier　survival　analysis　revealed　significant　differences　between　high-　and　low-risk　groups　identified　by　the　model.　The　ResNet-CBAM　model　outperformed　those　in　the　TM　and　ZC　groups　in　distinguishing　high-risk　patients　and　predicting　both　short-　and　long-term　survival.　Compared　to　machine　learning　models,　it　demonstrated　superior　performance　in　long-term　survival　prediction.Conclusion　The　ResNet-CBAM　model　trained　on　tumor-lymph　projection　planes　effectively　distinguished　high-risk　esophageal　cancer　patients　and　outperformed　traditional　models　in　predicting　survival　outcomes.　By　capturing　spatial　relationships　between　tumors　and　lymph　nodes,　it　demonstrated　enhanced　predictive　efficiency.