In　neuromorphic　computing,　the　coding　method　of　spiking　neurons　serves　as　the　foundation　and　is　crucial　for　various　aspects　of　network　operation.　Existing　mainstream　coding　methods,　such　as　rate　coding　and　temporal　coding,　have　different　focuses,　and　each　has　its　own　advantages　and　limitations.　This　paper　proposes　a　novel　coding　scheme　called　activeness　coding　that　integrates　the　strengths　of　both　rate　and　temporal　coding　methods.　It　encompasses　precise　timing　information　of　the　most　recent　neuronal　spike　as　well　as　the　historical　firing　rate　information.　The　results　of　basic　characteristic　tests　demonstrate　that　this　encoding　method　accurately　expresses　input　information　and　exhibits　robustness.　Furthermore,　an　unsupervised　learning　method　based　on　activeness-coding　triplet　spike-timing　dependent　plasticity　(STDP)　is　introduced,　with　the　MNIST　classification　task　used　as　an　example　to　assess　the　performance　of　this　encoding　method　in　solving　cognitive　tasks.　Test　results　show　an　improvement　in　accuracy　of　approximately　4.5%.　Additionally,　activeness　coding　also　exhibits　potential　advantages　in　terms　of　resource　conservation.　Overall,　activeness　offers　a　promising　approach　for　spiking　neural　network　encoding　with　implications　for　various　applications　in　the　field　of　neural　computation.