<p>In recent years, Dempster-Shafer evidence theory has been widely applied in multi-source information fusion. To address the unreasonable results under highly conflicting evidence, many methods have been proposed, particularly uncertainty-based weighting methods. However, these methods exhibit a negative weighting phenomenon in certain scenarios, where abnormal evidence is assigned higher weight than normal evidence. This paper proposes a multi-uncertainty clustering method by systematically analyzing the limitations of uncertainty-based weighting methods. We employ Spearman correlation coefficients to select appropriate uncertainty measures. These selected measures are calculated for evidence sources and then input into an improved K-Means algorithm for evidence clustering. For each formed evidence cluster, average evidence is generated to enhance the expression of intra-cluster common features. The support degree of different categories is then quantified based on cluster size. Furthermore, this research designs a composite weight that combines cluster weight with similarity weight, providing a comprehensive evaluation of evidence reliability from both macro-level category differences and micro-level similarity dimensions. Experimental results demonstrate that the proposed method not only resolves the negative weighting problem in existing uncertainty-based weighting methods but also effectively handles highly conflicting evidence, showing advantages in pattern recognition and other application scenarios.</p>