The ¹³C urea breath test is widely used as the “gold standard” for detecting Helicobacter pylori domestically and internationally. Accurate measurements of carbon and oxygen isotope characteristics in CO₂ are significant for disease diagnosis. Tunable diode laser absorption spectroscopy (TDLAS) has been widely used in multiple fields due to its simple structure, fast response speed, and high sensitivity. It is also fully suitable for the measurement and research of gas isotopes. In this study, a quantum cascade laser with a central wavelength of 4.32 μm and a small volume gas absorption cellwith an optical path of 14 cm and volume of 44 mL was used to simultaneously measure the volume concentrations of ¹⁶O¹²C¹⁶O，¹⁸O¹²C¹⁶O and ¹⁶O¹³C¹⁶O in CO₂ based on direct absorption spectroscopy. In addition, the noise interference caused by the stability of the light source in the direct absorption spectrum system and gas sample fluctuations were reduced using the Back Propagation (BP) neural network model. The results showed that the measurement accuracy and stability of isotope abundance based on the BP neural network model were better than those of the absorbance peak ratio method. The concentration measurement accuracy of ¹⁶O¹³C¹⁶O and ¹⁸O¹²C¹⁶O increased by about 1.27 and 1.58 times, respectively. Meanwhile, the Allan variance analysis also showed that when the integration time was 106 s, the accuracy of ¹³C and ¹⁸O isotope abundance using the BP neural network model was 0.97‰ and 1.47‰, respectively, which improved about 2.1 times and 1.2 times compared to the absorbance peak ratio method. This fully proves the feasibility of the isotope abundance measurement method based on the BP neural network model, laying the foundation for developing high-precision isotope abundance sensors.