Continual　learning,　also　known　as　lifelong　learning,　addresses　the　fundamental　challenge　of　enabling　machine　learning　models　to　learn　from　a　continuous　stream　of　data　and　adapt　their　knowledge　to　changing　environments,　without　forgetting　previously　acquired　information.　This　paradigm　emulates　the　human　ability　to　accumulate　knowledge　and　skills　over　time,　allowing　models　to　build　upon　their　past　experiences　to　enhance　their　performance　in　new　tasks.　To　this　end,　continual　learning　algorithms　need　to　learn　robust　and　stable　representations　that　can　generalize　to　new　data.　In　this　paper,　we　present　an　algorithm　which　can　learn　more　concise　and　semantic　representations　that　are　beneficial　for　continual　learning　based　on　information　bottleneck　and　metric　learning.　Combined　with　Core　Set　which　leave　a　small　part　of　data　from　previous　tasks,　our　algorithm　attains　high　performance　on　benchmark　datasets:　MNIST　in　solving　continual　incremental　tasks.　©　2023　IEEE.