<p>This paper proposes a model of <i>N</i>-soft fuzzy expert set inspired by the notions of <i>N</i>-soft set, soft expert set, and fuzzy set. As time goes by, the existing evaluation results may not keep pace with the times, so the expert team needs to conduct further evaluation. The existing soft expert set and its hybrid models only consider whether experts agree with the existing evaluation results, but do not consider the ambiguity of experts when expressing their opinions. However, the new model takes into account the uncertainty of experts when they express their opinions about the objects’ grades, which is a deep optimization of the soft expert set. It can handle uncertainty problems involving multiple experts, and binary or nonbinary data types. Related operations and propositions are derived. We propose a priority score comparison table method in uncertain decision-making. This method considers both the degree of priority among different objects and limits unconditional compensability among parameter values. Moreover, it adds constraints on the reliability of the expert’s evaluation opinions, and decision-makers can set relevant thresholds according to their preferences and the actual situation. Finally, the effectiveness of the new model and the algorithm is verified by comparing and analyzing the proposed algorithm with the comparison table method and the score function method.</p>