Ultra-High　performance　concrete　(UHPC)　has　garnered　considerable　attention　in　the　construction　industry　due　to　its　exceptional　mechanical　properties　and　durability.　In　the　design　of　UHPC　beams,　accurately　predicting　shear　strength　is　crucial.　Inspired　by　the　dense　blocks　in　DenseNet,　this　paper　proposes　a　novel　neural　network　for　predicting　the　shear　strength　of　UHPC　beams.　A　comprehensive　database　of　UHPC　beams　was　initially　established　through　extensive　literature　data　collection,　amassing　619　experimental　data　samples.　Preprocessing　was　subsequently　conducted　using　mahalanobis　distance　(DM)　to　eliminate　outliers.　Subsequently,　the　database　underwent　detailed　parameter　analysis.　In　practical　testing,　the　novel　neural　network　model　exhibited　superior　accuracy　in　predicting　the　shear　strength　of　UHPC　beams,　surpassing　traditional　machine　learning　(ML)　models　and　empirical　formulas.　Furthermore,　it　demonstrated　excellent　generalization　capabilities,　achieving　an　R2　value　of　0.988　in　the　training　set　and　0.964　in　the　test　set.　The　study　introduced　the　shapley　additive　explanations　(SHAP)　method　to　conduct　global　and　local　interpretability　analysis　of　the　network　model,　demonstrating　the　contributions　of　each　input　parameter　to　the　model　predictions.　Finally,　based　on　this　network　model,　a　graphical　user　interface　(GUI)　was　developed　for　researchers　engaged　in　UHPC　beam　design,　enabling　users　to　easily　input　relevant　parameters　and　obtain　shear　strength　predictions.　The　findings　of　this　study　are　expected　to　promote　the　application　of　ML　models　in　predicting　the　complex　nonlinear　behavior　of　UHPC　structures,　providing　strong　support　for　engineering　practice.