<p>The rapid growth of real-world data highlights the need for advanced similarity measures to support pattern recognition and multi-criteria decision-making (MCDM). This paper introduces new cosine and cotangent similarity measures within the framework of fractional q-rung picture fuzzy hypersoft sets (F<i>q</i>-RPFHSS), an extension of picture fuzzy hypersoft sets that allows a multi-argument domain for parameter proximity. Weighted versions of the proposed measures are developed, and their fundamental properties, including boundedness, non-degeneracy, symmetry, and transitivity, are established through formal theorems. To illustrate their effectiveness, a hypothetical decision-making example and a pattern recognition application are presented, demonstrating the flexibility and reliability of the proposed framework in handling uncertainty. The findings confirm that the introduced similarity measures and the F<i>q</i>-RPFHSS environment provide a powerful and adaptable tool for decision analysis and pattern recognition, offering broader applicability than existing fuzzy soft and hypersoft set models.</p>