In view of the importance of the concentration of engine tail flame components to the infrared spectrum radiation intensity, an efficient infrared spectrum concentration solution model is proposed, namely the CARS-CNN-GRU model which combines the competitive adaptive reweighted sampling (CARS) algorithm with the convolutional neural network (CNN)-gated recurrent unit (GRU) deep learning algorithm. This method uses the CARS algorithm to select the key wavelengths and obtain the tail flame component concentration information. Then the CNN-GRU model is used to perform long-range dependency analysis on the sequence data to achieve multi-scale feature extraction. Simulation results show that compared with the traditional models, the CARS-CNN-GRU model has higher accuracy in solving H₂O and CO₂ concentrations. Its root mean square error (RMSE) is reduced to 0.0014 and 0.0017, respectively. The R² value is 0.999 and 0.998, respectively; the mean absolute error (MAE) is 0.0011 and 0.0014, respectively. The CARS-CNN-GRU model proposed in this paper shows superior performance in solving infrared spectral concentration. Compared with traditional methods, it has higher accuracy, stability and reliability, and provides strong support for stealth technology, environmental monitoring and combustion efficiency evaluation in the military and civil aviation fields.