A pangenome graph can represent the genomes of multiple individuals simultaneously. It provides a more comprehensive reference and overcomes the allele bias caused by linear reference genome, which is becoming a powerful new source of information in research. Sequence-to-graph alignment is the computational core of many pangenome related tasks, which aligns sequences to a graph to find the best matches. It serves as a crucial link for upstream and downstream research applications. However, existing sequence-to-graph alignment algorithms can not handle large-scale sequences efficiently. In this paper, we propose a vectorization version of sequence-to-graph alignment algorithm. We vectorize the core of Gwfa with SIMD instructions to improve the speed of per sequence alignment. Experiments on real and simulated datasets showed that, our algorithm achieved speedups of about 1.5 \(\times \) , while maintaining the alignment result consistent.