Self-adaptation　has　always　been　a　hot　topic　in　the　interdisciplinary　field　of　software　engineering　and　service　computing.　By　perceiving　the　changes　of　themselves　and　the　environment,　applications　dynamically　adjust　their　behaviors　and　processes　to　continue　achieving　service　goals　efficiently　under　the　circumstances　of　the　non-deterministic　changes　of　environment　and　requirements.　With　the　recent　development　of　big　data　and　artificial　intelligence(AI),　traditional　model-based　control　methods　in　software　engineering　are　no　longer　suitable　for　dynamic　and　complex　service　computing　environments　nowadays.　In　contrast,　the　data-driven　approach　does　not　rely　on　mathematical　models　and　expert　knowledge　but　is　based　on　probability　and　mathematical　statistics.　By　applying　the　feedback　data　of　service　operation,　the　approach　gradually　learns　and　understands　the　complex　and　changeable　environmental　feedback,　and　then　learns　the　model　of　the　adaptive　system.　Therefore,　the　data-driven　self-adaptive　service　computing　has　the　characteristics　of　perceptibility,　adaptability,　autonomy　and　collaboration,　etc.　It　is　suitable　for　more　complex　application　scenarios,　such　as　the　Internet　of　things,　intelligent　transportation　and　distributed　computing.　Based　on　the　self-adaptive　framework　and　the　related　characteristics　of　cognitive　computing,　a　data-driven　intelligent　adaptive　framework　is　proposed.　And　then,　we　have　reviewed　the　application　of　representation　learning,　pattern　recognition,　decision　planning　and　rule　evolution　in　data-driven　adaptive　technology　in　recent　years,　respectively.　It　mainly　explores　the　application　of　machine　learning,　deep　learning　and　reinforcement　learning　in　these　technologies.　And　finally,　it　concludes　the　development　of　self-adaption　and　looks　forward　to　the　future　trends.　©　2022,　Science　Press.　All　right　reserved.