Abstract: To address the problem of multi-attribute matching decision-making, in which both the attributes and attribute weights are interval-value intuitionistic fuzzy numbers (IVIFNs), we propose an approach for matching decision-making. Based on the definition of weighted absolute distance for IVIFNs, we develop fractional programming models to realize an optimal degree of matching between two-sided agents, using the order preference technique of similarity to reach an ideal solution. Via Charnes and Cooper transformations, we transform these nonlinear models into linear programming models. Then, by solving these models, we obtain interval-value matrices for the degree of matching. In addition, we develop a double-objective interval optimization model that maximizes the degree of matching on both sides. Using the linear weighted method, we convert these models into a single-objective optimization model. Matching results can then be reached by solving this model. An example analysis illustrates the validity and feasibility of the proposed method.