A near-infrared methane (CH₄) sensor system for carbon isotopic abundance analysis was developed based on laser absorption spectroscopy (LAS). For good thermal stability, two CH₄ absorption lines with a similar low-state energy level were selected to realize relative weak temperature dependence. Wavelet denoising (WD) was employed for a pre-treatment of the direct absorption spectral (DAS) signal to perform a preliminary suppression of high-frequency noise. Due to the abnormal ¹³CH₄ profile caused by superimposition of multiple lines, two statistical analysis algorithms including linear regression and neural network prediction were respectively employed on the retrieval of molecule fractions instead of the traditionally used standard absorption line fitting method. Performance assessment and a comparison between the two methods were carried out. Compared with the concentration deducing method based on the maximum absorbance in rough data, the linear regression and the neural network prediction obtained a sensitivity enhancement by ∼2 times and ∼10 times, respectively. A simultaneous measurement of pressure and concentration was performed using the neural network, which indicated a good potential of the technique for multi-parameter analysis using a single LAS-based sensor system.