###### Abstract
By grouping similar alternatives into classes, cluster analysis reduces data dimensionality and facilitates comprehension and processing. Unlike supervised learning techniques, cluster analysis is an unsupervised learning approach primarily aimed at identifying natural clusters or groups when dataset labels are ambiguous or challenging to determine. However, it can only categorize alternatives as either similar or dissimilar without providing information on their relative superiority or importance. Artificial neural networks (ANN) have gained traction in MCDM for addressing complex problems and approximating multi-attribute utility functions by learning the mapping relationship between input and output, but their successful integration requires substantial data support to optimize performance. The analytic hierarchy process (AHP) is extensively utilized in the field of multi-criteria decision-making and has demonstrated its effectiveness in addressing performance decision-making issues of rescue robots [11]. [PERSON] et al. [2] have utilized AHP to evaluate the walking performance of walking mechanism in coal mine rescue robots. [PERSON], [PERSON] et al. [12] have employed AHP to evaluate the technical indexes, performance indexes, and structural indexes for scheme selection during the design and manufacturing process of new water-wading rescue robots. The primary advantages of AHP lie in its ability to simultaneously handle multiple criteria, analyze complex problems hierarchically, and provide a clear and comprehensible model structure. Moreover, the AHP method effectively integrates qualitative and quantitative data, enabling decision-makers to consider both types of information when making decisions and facilitating more comprehensive and accurate evaluations. However, it is important to note that the process of factor comparison often entails inherent uncertainty and subjectivity, as experts may encounter difficulties in establishing a precise scale for comparing two factors. Under such circumstances, employing the classical AHP method may yield decision results that lack accuracy. As an effective approach for handling incomplete or fuzzy information, the fuzzy analytic hierarchy process (FAHP) offers a more comprehensive and scientifically grounded basis for decision-making in the presence of incomplete information and complex causal relationships among variables. The fundamental concept behind FAHP lies in its consideration of interdependencies between indexes, enabling a clearer understanding of how changes in one index impact others. Consequently, FAHP method has gained widespread application across various domains. For instance, in control engineering, it can optimize control system design; within artificial intelligence [13], it addresses fuzzy logic problems; management science benefits from its ability to tackle complex decision-making challenges [14]; and multi-criteria decision-making relies on FAHP as an indispensable tool. However, the aforementioned evaluation methods lack a visual mode, which hinders the clear interpretation of the evaluation results. This not only obscures the significance of each index but also complicates the ranking of rescue robots based on their performance. The radar chart method takes into account both the overall strength and coordinated development across various aspects, thereby providing a more comprehensive reflection of an evaluated object's advantages [15]. The most notable feature of radar charts lies in their capacity to visually represent the status of a rescue robot and accurately rank similarly performing rescue robots, thereby facilitating a more comprehensive understanding of their performance [16; 17]. However, the weight of each index is fixed and cannot be adjusted according to the actual circumstances in the conventional radar chart. This limitation may result in an underestimation of the pivotal role played by certain indexes during the evaluation process, as well as an overestimation of the significance of other indexes.