It is notable that dynamic learning can all the while improve the nature of the grouping model and decline the unpredictability of preparing examples. In any case, a few past contemplates have shown that the exhibition of dynamic learning is effectively disturbed by an imbalanced information dispersion. A few existing imbalanced dynamic taking in approaches additionally experience the ill effects of either low execution or high time utilization. To address these issues, this paper depicts a productive arrangement dependent on the outrageous learning machine (ELM) grouping model, called dynamic online-weighted ELM (AOW-ELM). The principle commitments of this paper include: 1) the reasons why dynamic learning can be upset by an imbalanced case conveyance and its affecting components are talked about in detail; 2) the various leveled grouping strategy is embraced to choose at first named occasions so as to dodge the missed groupĀ  impact and cold beginning wonder however much as could be expected; 3) the weighted ELM (WELM) is chosen as the base classifier to ensure the unbiasedness of occurrence determination in the technique of dynamic learning, and an effective online refreshed method of WELM is found in principle; also, 4) an early halting model that is like however more adaptable than the edge weariness model is exhibited. The exploratory outcomes on 32 paired class informational indexes with various irregularity proportions show that the proposed AOW-ELM calculation is more powerful and effective than a few state-of the-workmanship dynamic learning calculations.