Heart　rate　(HR)　is　an　important　indicator　reflecting　the　overall　physical　and　mental　health　of　the　human　body,　playing　a　crucial　role　in　diagnosing　cardiovascular　and　neurological　diseases.　Recent　research　has　revealed　that　variations　in　the　light　absorption　of　human　skin　captured　through　facial　video　over　the　cardiac　cycle,　due　to　changes　in　blood　volume,　can　be　utilized　for　non-contact　HR　estimation.　However,　most　existing　methods　rely　on　single-modal　video　sources　(such　as　RGB　or　NIR),　which　often　yield　suboptimal　results　due　to　noise　and　the　limitations　of　a　single　information　source.　To　overcome　these　challenges,　this　paper　proposes　a　multimodal　information　fusion　architecture　named　the　spatiotemporal　sensitive　network　(SS-Net)　for　non-contact　heart　rate　estimation.　Firstly,　spatiotemporal　feature　maps　are　utilized　to　extract　physiological　signals　from　RGB　and　NIR　videos　effectively.　Next,　a　spatiotemporal　sensitive　(SS)　module　is　introduced　to　extract　useful　physiological　signal　information　from　both　RGB　and　NIR　spatiotemporal　maps.　Finally,　a　multi-level　spatiotemporal　context　fusion　(MLSC)　module　is　designed　to　fuse　and　complement　information　between　the　visible　light　and　infrared　modalities.　Then,　different　levels　of　fused　features　are　refined　in　task-specific　branches　to　predict　both　remote　photoplethysmography　(rPPG)　signals　and　heart　rate　(HR)　signals.　Experiments　conducted　on　three　datasets　demonstrate　that　the　proposed　SS-Net　achieves　superior　performance　compared　to　existing　methods.