###### Abstract
To avoid losing linguistic information in the decision-making process, [PERSON] and [PERSON] [18] introduced a binary semantic computing model, which could directly calculate semantic terms, and the results of the set formation and operation often corresponded to the semantically evaluated information defined in advance, ensuring the completeness of the linguistic information. In the early research, [PERSON] [19] applied the binary linguistic representation model in a multigranular linguistic context to construct the HOQ. [PERSON] [20] employed a binary linguistic computation technique to create Failure Mode and Effects Analysis (FMEA) utilizing the HOQ framework. [PERSON] and [PERSON] [21] amalgamated fuzzy data and binary semantic models within the context of QFD for the determination of supplier selection criteria weights and supplier ratings. Recently, [PERSON] et al. [22] introduced a novel fuzzy linguistic representation model for comparing linguistic expressions. This model capitalizes on the strengths of the binary linguistic representation model to enhance the interpretability and precision of results in the word assignment process, thereby extending the range of comparative linguistic expressions with symbolic translation. On the other hand, QFD represents a collaborative decision-making approach typically executed by an interdisciplinary team comprising professionals from marketing, design, quality, and production, and may also include customer participation [23]. Therefore, several Multi-Criteria Decision Making (MCDM) techniques have been applied to augment conventional QFD. [PERSON] et al. [24] introduced an innovative integrated MCDM method to improve QFD, employing HFLTS to address fuzziness in the evaluation process. They used fuzzy DEMATEL to determine CRs' weights and then introduced an extended MULTIMOORA method, combining entropy weighting, to rank ECs. [PERSON] et al. [25] devised an interval-valued spherical fuzzy ORESTE approach, which relied on a three-dimensional HOQ model for EC ranking. [PERSON] et al. [26] proposed two new aggregation operators and applied them to develop a systematic approach for handling multi-attribute decision-making (MADM) scenarios involving complex intuitionistic fuzzy data in the context of design. TOPSIS is a commonly used and efficient method in MCDM that comprehensively, reasonably, and accurately ranks various alternatives. Compared to the traditional multivariate statistical analysis methods used for evaluation problems, the TOPSIS method has the advantages of intuitive analytical principles, simple calculations, and low sample requirements. In recent years, it has been successfully applied to address various MCDM problems [27; 28]. However, one challenge with the TOPSIS method is the requirement for external attribute weights. In past research, the Analytic Hierarchy Process (AHP) was commonly used to calculate CRs' weights in QFD. Although pairwise comparisons between attributes ensured comprehensive evaluation, inconsistencies often arise among decision-makers, especially when dealing with a large number of alternative solutions, increasing the complexity of solving MCDM problems. [PERSON] [29] introduced the Best-Worst Method (BWM), which reduces the number of pairwise comparisons by establishing a new pairwise comparison structure. This structure only compares the best and worst attributes with others, thereby reducing the inherent inconsistency in pairwise comparisons as the number of alternative solutions increases. As a result, BWM has found extensive application in the decision-making field [30; 31]. For instance, [PERSON] and [PERSON] [32] introduced Fuzzy BWM, an extension of traditional BWM in a fuzzy environment. This method replaces discrete pairwise comparison matrices with linguistic expressions of fuzzy comparative judgments, making it more convenient for decision-makers.