With advancements in sequencing technology, human genome analysis has shifted from individual analysis to population analysis. To better demonstrate the genetic variation information between different samples within a population, the pan-genome graph model has replaced the traditional linear multi-sequence reference genome model, and sequence-to-graph alignment has become a key issue in biological sequence analyses. Existing alignment algorithms employ seed-and-extend strategies. However, owing to the numerous paths formed by graph combinations, localization and verification phases become time-consuming, necessitating further optimization and improvement of single-seed selection methods. To address this issue, this paper proposes a sequence alignment algorithm based on a combined minimizer seed. In the localization phase, the algorithm enhances the coverage range of a single seed through the combined hashing of minimizer seeds. Simultaneously, seeds are located through both sequence and relative position information, which significantly reducing the number of false-positive matching positions, thus lowering the workload of the subsequent filtering and verification processes. Experimental results demonstrate that the proposed algorithm can reduce candidate positions by approximately 80%, optimize time performance by one to three times, and have index memory and precise comparison capabilities comparable to mainstream alignment algorithms.