Abstract: :Probabilistic linguistic term set (PLTS),composed of linguistic terms and their probability information,can effectively express uncertainties.When dealing with the problem of different numbers of linguistic terms in PLTS,this paper proposes a normalization method based on the greatest common divisor to make all terms have the same probability.Subsequently,a new parameterized distance is designed.By setting parameter values to represent terms of different scales,it solves the limitation that existing distance formulas rely on linguistic subscripts or specific scaling functions.In addition,for the case where attribute weights are unknown,this paper constructs a hybrid weight model by combining information entropy and dispersion to calculate weight information.Finally,a PLTS multi-attribute decision-making method is proposed by integrating the TOPSIS method,and the effectiveness and super-iority of the method are verified with the example of subway site selection.The results show that the method has strong applicability in both theory and practice.

Key words: probabilistic linguistic term set（PLTS）, parametric distance, hybrid weight, standardization method, multipleattribute decisionmaking