To　solve　the　problem　of　concept　drift　in　complex　industrial　process　and　to　improve　the　generalization　performance　of　ensemble　learning　model,　an　ensemble　modeling　method　of　online　dynamic　selection　for　optimizing　the　diversity　of　the　base　learners　was　proposed,　on　the　basis　of　ensuring　the　accuracy　of　the　ensemble　learning　model.　Online　sequential　extreme　learning　machine　was　used　as　the　base　learner,　and　the　base　learners　were　sorted　in　reverse　order　according　to　their　classification　accuracy　on　the　sliding　window.　The　other　performance　indexes　of　the　basic　learners　on　the　sliding　window　were　used　as　the　feature　attributes,　and　the　approximate　linear　dependence　condition　was　used　to　select　accurate　and　diverse　base　learners　for　ensemble　output,　which　improves　the　classification　accuracy　of　the　ensemble　algorithm　in　dealing　with　the　concept　drift　data　stream.　Finally,　the　rationality　and　effectiveness　of　the　proposed　algorithm　were　verified　by　using　the　synthetic　data　sets　and　real-world　data　sets.　©　2021,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.