Deep learning methods used in the field of gas detection mostly focus on learning a single task, such as the qualitative classification of gas or the quantitative regression of gas concentration. However, training a model in this way ignores the correlation of information between related tasks, reducing the accuracy and efficiency of training. This paper proposes a multi-task learning (MTL) model that combines a one-dimensional convolutional neural network (1DCNN) and a long short-term memory (LSTM) network to realize qualitative identification of mixed gas species in parallel with a quantitative regression prediction of gas concentrations. Using a thulium-doped fiber, a two-stage amplified thulium-doped ring-cavity fiber laser was constructed, and the absorption spectral data of mixed gases, comprising CO₂ and NH₃, were detected based on the active intracavity absorption spectroscopy method. The experimental data were put into the MTL model to train until the model performance was optimized. The trained model achieves a gas classification accuracy rate of 100%, while the coefficient of determination of NH₃ and CO₂ are 99.86% and 99.62%, respectively. These values are superior to the equivalent values obtained using conventional single-task models and gas inversion algorithms such as the backpropagation neural network and support vector machine. By combining the deep learning algorithm with the active intracavity spectroscopy method, a superior absorption spectroscopy-based gas inversion technology is developed.