双光梳光谱因具备高分辨率和快速扫描优势，在燃烧诊断与气体定量检测中具有广泛应用。然而在实际测量中，单帧双光梳信号信噪比较低，导致基于最小二乘拟合的参数反演精度显著下降。针对该问题，本文提出一种基于零均值归一化互相关（ZNCC）的双光梳光谱参数反演网格搜索方法。该方法通过构建温度–压强二维时域模板库，对单条吸收光路下的等效线平均测试干涉信号与模板信号进行ZNCC匹配，并结合归一化与二维样条插值，以相似度峰值确定最优参数。仿真结果表明，在无噪声条件下温度与压强的相对误差均小于0.1%，在 15 dB 信噪比下仍可保持1%–2%的预测精度。与最小二乘方法相比，该方法具有更强的抗噪性与计算稳定性，可实现单帧快速反演。

Dual-comb spectroscopy (DCS), with its high resolution and rapid scanning capability, has been widely applied in combustion diagnostics and quantitative gas detection. However, under low signal-to-noise ratio (SNR) conditions, the parameter retrieval accuracy of traditional least-squares-based spectral fitting methods deteriorates significantly. To address this issue, this study proposes a dual-comb spectroscopic grid-search algorithm based on zero-mean normalized cross-correlation (ZNCC). The method constructs a two-dimensional temperature–pressure template library, computes the ZNCC between the equivalent line-averaged test interferogram obtained along a single absorption path and the template signals, and normalizes and interpolates the correlation coefficient matrix using a two-dimensional spline. The optimal temperature and pressure are then determined from the correlation peak position. Simulation results demonstrate that the proposed algorithm achieves relative errors below 0.1% for both temperature and pressure under noise-free conditions, and maintains 1–2% prediction accuracy at an SNR of 15 dB. Compared with conventional least-squares fitting approaches, the ZNCC-based grid-search method exhibits stronger noise immunity and computational stability, enabling single-frame, fast parameter retrieval.