Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation (PCT) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components (PCs) necessary to represent the data. Subsequently, a k-d tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches.