Based　on　the　relevant　theories　of　M-matrix　and　the　Lyapunov　stability　technique,　this　paper　investigates　the　multistability　of　equilibrium　points　and　periodic　solutions　for　Clifford-valued　memristive　Cohen-Grossberg　neural　networks.　With　the　help　of　Cauchy　convergence　criterion,　the　exponential　stability　inequality　is　derived.　The　system　has　[Pi(A)(K-A+1)](n)　locally　exponentially　stable　equilibrium　points　and　periodic　solutions,　which　greatly　increases　the　number　of　solutions　compared　with　the　existing　Cohen-Grossberg　neural　networks,　where　the　K-A　is　a　Clifford-valued　and　there　is　no　limitation　of　linearity　and　monotonicity　for　activation　functions.　Furthermore,　the　attraction　basins　of　stable　periodic　solutions　are　obtained,　and　it　is　proved　that　the　basins　can　be　enlarged.　It　is　worth　mentioning　that　the　results　can　also　be　used　to　discuss　the　multistability　of　equilibrium　points,　periodic　solutions,　and　almost　periodic　solutions　for　real-valued,　complex-valued,　and　quaternion-valued　memristive　Cohen-Grossberg　neural　networks.　Finally,　two　numerical　examples　with　simulations　are　taken　to　verify　the　theoretical　analysis.