In　dynamic　environments,　making　classification　decisions　based　on　classical　intelligent　decision　support　systems　is　a　challenge,　as　the　classification　performance　of　decision-making　and　the　time-cost　of　learning　need　to　be　considered　simultaneously.　Moreover,　many　tasks　of　classification　decisions　lack　label　information　because　annotating　data　is　time-consuming,　labor-intensive　and　expensive　process.　This　means　that　some　standard　intelligent　decision　support　systems　will　perform　inferior　performance　if　they　cannot　dynamically　make　full　use　of　the　information　behind　abundant　unlabeled　data.　Therefore,　by　incorporating　knowledge　representation　and　dynamic　updating　mechanisms　into　concept　learning　processes,　we　introduce　a　novel　dynamic　concept　learning　approach,　namely　semi-supervised　concept-cognitive　computing　system　(s2C3S),　for　making　classification　decisions　by　jointly　utilizing　some　labeled　data　and　abundant　unlabeled　data　under　dynamic　environments.　A　theoretical　analysis　has　shown　that　the　proposed　s2C3S　can　achieve　significantly　lower　computational　costs　and　higher　classification　accuracies　than　the　existing　incremental　K　Nearest　Neighbor　method　(IKNN)　and　concept-cognitive　computing　system　(C3S).　The　experimental　results　on　various　datasets　further　demonstrated　that　our　system　is　effective　for　dynamic　classification　decision-making　with　limited　labeled　data　under　dynamic　learning　processes.　Additionally,　s2C3S　can　also　be　applied　to　computer-assisted　intelligent　diagnosis　from　the　given　medical　images　(such　as　chest　X-ray　images)　dynamically　and　accurately.　©　2024　Elsevier　B.V.