In　this　paper,　a　type　of　delayed　Clifford-valued　memristor-based　neural　networks　(CLVMNNs)　with　impulsive　disturbances　is　established,　and　the　global　exponential　stability　is　investigated　by　using　generalized　norm.　Firstly,　the　n-dimensional　Clifford-valued　systems　are　decomposed　into　2(m)　n-dimensional　real-valued　systems　to　address　the　non-commutativity　problem　of　the　multiplication　of　Clifford　numbers.　Secondly,　the　generalized　infinity-norm　and　1-norm　are　introduced　to　induce　the　global　exponential　stability　for　CLVMNNs,　and　two　special　Lyapunov　functionals　are　established　to　prove　the　stability.　Thirdly,　the　strict　assumption　of　the　boundedness　of　activation　function　in　previous　research　is　loosened,　and　some　less　conservative　conditions　of　stability　are　obtained　based　on　the　constructed　Lyapunov　functionals.　Finally,　the　theoretical　results　are　verified　by　two　numerical　simulations,　and　an　image　encryption　scheme　is　proposed　to　show　the　application　in　real　world　situation　based　on　the　delayed　CLVMNNs.