The　contemporary　landscape　of　medical　diagnostics　and　therapeutic　interventions　has　witnessed　a　remarkable　surge　in　the　production　of　time　series　data.　Artificial　intelligence　(AI),　particularly　the　deep　learning,　has　presented　promising　values　in　investigating　the　high-dimension　and　meaningful　significance　hidden　behind　these　diagnostic　data.　In　this　work,　we　propose　a　novel　analytics　for　intelligent　nucleic　acid　amplification　tests　(NAAT)　based　on　deep　learning　and　paper　microfluidics.　On-chip　amplification　data　were　straightforwardly　fed　to　a　deep　learning　model　derived　from　Transformer　neural　network.　To　facilitate　the　development　and　deployment　of　the　approach,　we　conducted　a　lightweight　processing　of　the　Transformer　model.　Then,　the　capacity　of　the　model　for　accurately　predicting　the　reaction　trend　and　end-point　value　was　validated.　We　also　employed　ablation　experiments　to　evaluate　the　effects　of　various　parameters　on　prediction　performance　followed　by　optimizing　the　model.　Then,　three　clinical　datasets　including　706　positive　and　205　negative　samples　obtained　from　Fujian　Provincial　Hospital　were　used　to　verify　the　generalization　of　the　approach.　Without　any　modification　of　the　model　structure　and　hyperparameters,　accuracy,　sensitivity,　and　specificity　by　the　presented　approach　were　98.28　%,　97.52　%　and　99.02　%.　Further　comparison　studies　based　on　the　nine　different　AI　algorithms　including　recurrent　neural　network　and　long-short　term　memory　were　performed.　The　presented　study　holds　potential　to　facilitating　routine　diagnostic　tasks　for　preventing　pandemic　and　propelling　the　development　of　smart　portable　instruments.