Abstract: Laser spectroscopic analysis is one of the most important techniques for quantitative gas analysis in industrial environments. However, the measurement accuracy of current laser-based gas analyzers is strongly affected by variations in ambient pressure, leading to deviations of the detected data from the true gas concentration under different pressure conditions. To address this issue, an industrial gas pressure-compensation experimental platform was constructed, and multiple repeated experiments were conducted within the pressure range of 60–140 kPa. The results revealed that the measured CO₂ concentrations were higher than the standard gas concentration under negative pressure and lower under positive pressure. To improve the accuracy of laser gas sensors, a pressure compensation algorithm combining the Back Propagation (BP) neural network and Particle Swarm Optimization (PSO) is selected, and the Gated Recurrent Unit (GRU) is adopted to predict the compensated data. On this basis, an improved integrated PSO–BP–GRU optimization algorithm is proposed. The BP network is utilized for static nonlinear mapping, while PSO is applied to optimize the weights and thresholds of the BP neural network; the PSO–BP hybrid algorithm thus enhances global search performance and compensation accuracy. The PSO–BP algorithm is employed to conduct nonlinear compensation and feature enhancement on raw signals (such as disturbed concentration or pressure values), acting as a preprocessing module connected to the GRU network. This provides the GRU with higher-quality input data, further improving its learning efficiency and prediction accuracy. Through the collaborative integration of the three algorithms, a multi-level compensation and prediction model adaptable to multi-pressure segment measurement of CO₂ concentration is constructed. The BP neural network was first employed to reduce the raw sensor errors, lowering the CO₂ measurement deviation to 2.6×10⁻⁵ after initial compensation. However, due to limited parameter generalization, some data points still exhibited large deviations. Subsequently, the PSO algorithm was introduced to optimize the BP network parameters, resulting in a significant improvement in compensation performance: the maximum absolute error between the measured and true concentrations among four datasets decreased to 2.288×10⁻⁶, and the minimum absolute error reached 1.2×10⁻⁸. Finally, by integrating GRU for gas concentration prediction, the normalized root mean square error (RMSE) and normalized mean absolute error (MAE) between the predicted and actual values were both less than 0.014 and 0.020, respectively. Experimental results demonstrate that the PSO–BP–GRU algorithm effectively enhances the measurement accuracy of laser gas sensors and successfully eliminates the influence of ambient pressure on detection results. Based on this compensation and prediction model, an industrial online gas analysis system (TZX−7000A series) was developed, enabling in-situ quantitative detection of ten gases including CO₂, SO₂, NO₂, NO, CO, CH₄, NH₃, and H₂S. The proposed model and method can be applied to chemical industrial parks, coal mining, and gas transportation.