Hypertension　is　a　major　risk　factor　for　many　serious　diseases.　With　the　aging　population　and　lifestyle　changes,　the　incidence　of　hypertension　continues　to　rise,　imposing　a　significant　medical　cost　burden　on　patients　and　severely　affecting　their　quality　of　life.　Early　intervention　can　greatly　reduce　the　prevalence　of　hypertension.　Research　on　hypertension　early　warning　models　based　on　electronic　health　records　(EHRs)　is　an　important　and　effective　method　for　achieving　early　hypertension　warning.　However,　limited　by　the　scarcity　and　imbalance　of　multivisit　records,　and　the　nonstationary　characteristics　of　hypertension　features,　it　is　difficult　to　predict　the　probability　of　hypertension　prevalence　in　a　patient　effectively.　Therefore,　this　study　proposes　an　online　hypertension　monitoring　model　(HRP-OG)　based　on　reinforcement　learning　and　generative　feature　replay.　It　transforms　the　hypertension　prediction　problem　into　a　sequential　decision　problem,　achieving　risk　prediction　of　hypertension　for　patients　using　multivisit　records.　Sensors　embedded　in　medical　devices　and　wearables　continuously　capture　real-time　physiological　data　such　as　blood　pressure,　heart　rate,　and　activity　levels,　which　are　integrated　into　the　EHR.　The　fit　between　the　samples　generated　by　the　generator　and　the　real　visit　data　is　evaluated　using　maximum　likelihood　estimation,　which　can　reduce　the　adversarial　discrepancy　between　the　feature　space　of　hypertension　and　incoming　incremental　data,　and　the　model　is　updated　online　based　on　real-time　data　using　generative　feature　replay.　The　incorporation　of　sensor　data　ensures　that　the　model　adapts　dynamically　to　changes　in　the　condition　of　patients,　facilitating　timely　interventions.　In　this　study,　the　publicly　available　MIMIC-III　data　are　used　for　validation,　and　the　experimental　results　demonstrate　that　compared　to　existing　advanced　methods,　HRP-OG　can　effectively　improve　the　accuracy　of　hypertension　risk　prediction　for　few-shot　multivisit　record　in　nonstationary　environments.