<p>Picture fuzzy sets address problems characterized by ambiguity, instability and inconsistent data. Similarity measures on picture fuzzy sets play an indispensable role in determining the relationships between two such sets. Consequently, the study of similarity measures for picture fuzzy sets has garnered significant attention from scholars, yielding fruitful results. Notably, the existing research on picture fuzzy set similarity has mainly focused on overcoming the limitations of certain existing similarity measures by proposing one or a few new ones, ignoring the construction methods for similarity measures. Therefore, this paper presents two novel construction methods for similarity measures on picture fuzzy sets. The first approach combines the differences among positive membership, neutral membership, negative membership, and refusal membership within picture fuzzy sets using a strictly monotonically decreasing function. Remarkably, this method not only integrates existing similarity measures but also generates novel ones, providing a unified framework for both. The second method employs a strictly decreasing binary function to aggregate the distance measures between two picture fuzzy sets. By varying the binary function and distance measures, we obtain a range of novel similarity measures. Additionally, we apply the newly developed similarity measures to pattern recognition and compare their performance against existing measures. Based on the identification results, it is evident that these novel similarity measures yield reasonable outcomes and exhibit a high degree of reliability.</p>