With　the　development　of　medical　diagnosis　and　treatment　intervention　techniques,　there　has　been　an　exponential　growth　in　medical　data　along　time　series.　Artificial　intelligence　(AI),　particularly　deep　learning　(DL),　has　demonstrated　significant　potential　in　uncovering　medical　data　along　time　series.　This　study　proposed,　for　the　first　time,　a　method　that　integrates　the　Transformer　architecture　with　the　Kolmogorov-Arnold　network　(KAN)　to　enable　predictive　analysis　of　nucleic　acid　amplification　experimental　data.　Through　experimental　data　analysis　methods,　the　effectiveness　of　the　model　in　accurately　predicting　amplification　trends　and　endpoint　values　was　validated,　achieving　an　endpoint　value　error　of　merely　1.87　and　an　R-square　coefficient　as　high　as　0.98.　Moreover,　the　model　was　capable　of　effectively　identifying　experimental　data　from　different　sample　types.　Furthermore,　this　research　delved　into　the　impact　of　the　model’s　components　and　parameters　on　predictive　performance　through　ablation　experiments　and　hyperparameter　tuning.　Finally,　a　generalization　capability　test　was　conducted　on　911　clinical　data　records　provided　by　the　Fujian　Provincial　Hospital　across　10　deep　learning　models.　The　results　demonstrated　that　the　proposed　Transformer-KAN　network　outperformed　other　models　in　terms　of　predictive　accuracy　and　generalization　capability.　This　study　not　only　provided　a　new　perspective　for　improving　routine　diagnostic　techniques　during　pandemics　but　also　offered　empirical　evidence　for　further　research　on　the　KAN　model　and　its　corresponding　foundational　theories.　©　2024　Editorial　of　Board　of　Journal　of　Graphics.　All　rights　reserved.