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more severe challenges and uncertainties than ever before. Hence, how to reliably and effectively evaluate whether corporates will exhibit substantial troubles/difficulties in the near future turns out to be an attractive investigative issue. This study introduces a fusion mechanism that gives decision makers a comprehensive description on a corporate's operation status so as to prevent a biased judgment from occurring. The introduced mechanism consists of three main procedures: (1) Performance rank determination through the integration of balanced scorecards (BSC) and two-level DEA with a weighting adjusted strategy; (2) Forecasting model construction by combining core vector machine (CVM) with the support vectors (SV)-based online learning strategy; and (3) Knowledge extraction by a rule-based algorithm. The experimental results show that the introduced fusion mechanism (i.e., TDMCR) reduces unnecessary information, gives a more overarching description, satisfactorily predicts a corporate's operation status, and provides intuitive decision logics for market participators to adjust their investment portfolios for maximizing their profit margins under an anticipated risk level. 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