An Improved Grey Target Method for Linguistic Multi-Criteria Classification Combined with Cloud Model and Case-Based Reasoning

Ignoring real decision contexts in linguistic multi-criteria classification (MCC) causes unreasonable weights, rank reversal and unreliable classification. This paper develops a context-constrained MCC framework integrating case-based reasoning, grey target theory and cloud model. Unlike conventional data-only grey target methods, three practical contextual features structurally reshape all algorithm modules: linguistic dual fuzziness-randomness, labeled benchmark cases and expert weight preferences. First, cloud Hamming distance and cloud-based dual target centers are constructed to eliminate rank reversal, a property invalid without linguistic uncertainty. Second, benchmark case constraints build a target-score classification mechanism with case-calibrated grading thresholds. Third, expert weight bounds form a bi-objective optimization model fusing multi-source information to avoid data-biased weights. A riparian wetland ecological grading case validates the method. A rigorous mathematical proof, counterexample contrast and large-scale random simulation are further supplemented to verify that the proposed fixed dual cloud target center rule significantly mitigates rank-reversal phenomena triggered by adding new alternatives under the designed experimental settings. Comparisons show our framework yields domain-consistent weights, grading outputs with perfect agreement with independent blind expert judgments, and stable monotonic scoring performance (where larger target scores correspond to worse ecological conditions), which achieves more consistent grading results compared with cloud entropy weighting and virtual linguistic approaches in the tested wetland case.