Transformative　technologies　have　the　potential　to　significantly　alter　existing　technological　paths　and　business　models,　accelerating　the　industrial　change　process.　Identifying　and　monitoring　transformative　technologies　early　is　critical　for　both　corporate　investment　decisions　and　government　research　&　development　(R　&　D)　strategy.　Current　studies　on　identifying　and　monitoring　transformative　technologies　have　primarily　focused　on　technologies　in　the　ascent　phase.　Few　research　performed　on　those　in　the　emerging　stage.　Given　gaps　in　current　research,　this　paper　proposes　an　early　identifying　framework　for　transformative　technologies　based　on　their　dynamic　evolution　process.　First,　we　discover　weak　signals　of　transformative　technology　using　Subject-Action-Objective　(SAO)　semantic　mining　and　an　outlier　detection　algorithm　based　on　discontinuity　features.　After　identifying　weak　signals,　our　research　methodology　involves　monitoring　the　developing　trends　from　two　different　perspectives.　Firstly,　we　scrutinize　whether　research　on　weak　signals　has　progressed　from　marginal　to　core　research　over　time　using　an　outlier　detection　algorithm.　Secondly,　we　investigate　the　convergence　of　weak　signals　with　respect　to　both　the　overall　technology　landscape　and　research　content　within　technology　levels.　This　is　achieved　through　the　utilization　of　the　Latent　Dirichlet　Allocation　(LDA)　and　Dynamic　topic　models　(DTM).　We　take　research　into　All-Solid　State　Battery　(ASSBs)　as　a　case　study.　The　results　present　the　ASSLBs　exhibit　characteristics　from　edge　to　core　study.　Also,　it　develops　two　primary　technology　tracks:　All-solid-state　lithium-ion　batteries　(ASSLIBs)　and　All-solid-state　lithium　Metal　batteries.　These　two　domain　technologies　display　a　development　pattern　from　divergence　to　convergence　research.　The　case　verifies　the　feasibility　and　validity　of　the　proposed　framework.　Our　work　contributes　to　provide　new　insights　into　identifying　disruptive　technologies　in　the　emerging　stage.　©　2023　PICMET　(Portland　International　Center　for　Management　of　Engineering　and　Technology(.