Traditional attribute reduction methods construct a unified attribute reduction set for decision classification， ignoring the differentiated representation of attributes among various decision classes， which often results in the pivotal attributes of specific classes being redundantly covered and leads to suboptimal classification accuracy for those specific classes. To address these issues， this paper proposes a class-specific attribute reduction method driven by conditional entropy based on fuzzy rough sets， considering the advantages of fuzzy rough sets in handling widely existing numerical and fuzzy data. Firstly， by integrating the decision inclusion degree of fuzzy rough sets with information entropy theory， a class-specific conditional entropy is defined to quantify the local discriminative power of conditional attributes with respect to the target class. Secondly， the paper presents a class-specific attribute reduction condition based on conditional entropy and defines both internal and external attribute significance measures based on this class-specific conditional entropy. Furthermore， forward （FA-CE） and backward （BA-CE） attribute reduction algorithms are proposed based on attribute significance. Finally， the class-specific attribute reduction is conducted on seven UCI datasets and two feature selection datasets， and comparative analyses are performed against methods based on neighborhood conditional entropy， mutual information， neighborhood rough sets， and a conventional dependency-based reduction approach. The classification accuracy and F₁-score of the proposed method are evaluated using support vector machine（SVM）， K-nearest neighbor（KNN） and classification and regression tree（CART） classifiers， demonstrating the rationality and effectiveness of the proposed class-specific attribute reduction approach.