Hypertension　is　a　major　cause　of　cardiovascular　diseases.　Accurate　and　convenient　measurement　of　blood　pressure　are　necessary　for　the　detection,　treatment,　and　control　of　hypertension.　In　recent　years,　face　video　based　non-contact　blood　pressure　prediction　is　a　promising　research　topic.　Interestingly,　face　diagnosis　has　been　an　important　part　of　traditional　Chinese　medicine　(TCM)　for　thousands　of　years.　TCM　practitioners　observe　some　typical　regions　of　the　face　to　determine　the　health　status　of　the　Zang　Fu　organs　(i.e.,　heart).　However,　the　effectiveness　of　face　diagnosis　theory　in　conjunction　with　computer　vision　analysis　techniques　to　predict　blood　pressure　is　unclear.　We　proposed　an　artificial　intelligence　framework　for　predicting　blood　pressure　using　deep　convolutional　neural　networks　in　this　study.　First,　we　extracted　pulse　wave　signals　through　652　facial　videos.　Then,　we　trained　and　compared　nine　artificial　neural　networks　and　chose　the　best　performed　prediction　model,　with　an　overall　true　predict　rate　of　90%.　We　also　investigated　the　impact　of　face　reflex　regions　selection　on　blood　pressure　prediction　model,　and　the　five　face　regions　outperformed.　Our　high　effectiveness　and　stability　framework　may　provide　an　objective　and　convenient　computer-aided　blood　pressure　prediction　method　for　hypertension　screening　and　disease　prevention.　©　2023