The rise of large language models has profoundly impacted natural language processing. With the growth of computational resources and the expansion of model sizes, the potential applications of large language models in natural language processing are increasingly evident. However, the widely used low-rank adaptation (LoRA) method faces challenges related to fine-tuning efficiency and storage costs as model sizes increase. To address this issue, this study proposes a singular value decomposition-based adaptation fine-tuning method. This method only requires the diagonal matrix and scaling vector obtained from singular value decomposition to be trainable parameters, achieving performance improvement in multiple natural language processing tasks while reducing training costs. Experimental results show that the proposed method outperforms other methods of the same order of magnitude in GLUE and E2E benchmark tests. Compared with commonly used parameter-efficient fine-tuning methods, it demonstrates significant advantages in reducing the number of trainable parameters and improving fine-tuning efficiency, achieving the highest performance gains in experiments on the fine-tuning efficiency of trainable parameters. Future research will focus on optimizing the proposed method to achieve more efficient fine-tuning in a wider range of tasks and larger-scale models.