## 1 Introduction
applied a PLSR model to accurately measure the mixing ratios and concentrations of gases from the photoacoustic spectra of H\({}_{2}\)O-NH\({}_{3}\)-CH\({}_{4}\) mixtures [21]. [PERSON] et al. employed a PLSR model to achieve accurate trace CH\({}_{4}\) concentration measurements from the optical emission spectra of low-temperature atmospheric pressure microplasmas [22]. [PERSON] et al. proposed an SVR-based method to accurately measure the concentrations of CO\({}_{2}\) and CH\({}_{4}\) in mixed gases based on the 1432 nm super-continuum laser absorption spectrum (SCLAS), with measurement accuracy better than k-nearest neighbors (KNN) and least squares (LS) methods [23]. [PERSON] et al. introduced a beetle antennae search algorithm (BAS) combined with competitive adaptive reweighted sampling (CARS) to improve the SVR modeling method, demonstrating superior accuracy and efficiency in the infrared spectral quantitative analysis of hydrocarbon gas componentscompared to traditional methods [24]. [PERSON] et al. proposed a method based on particle swarm optimization split peak fitting and support vector machines (PSS), combined with the PLSR model, to analyze the infrared spectra of mixed gases in oil wells and achieve accurate measurement of the concentration of each gas component [25]. However, these commonly used spectral quantitative analysis methods have certain limitations, such as the inability of the PLSR model to effectively fit nonlinear relationships and the susceptibility of the SVR model to local noise interference. To address the limitations of traditional infrared spectral quantitative analysis methods, deep learning techniques have been applied to spectral quantitative analysis tasks to enhance feature extraction and nonlinear modeling capabilities. Convolutional neural network (CNN) is a mainstream architecture in deep learning, playing a significant role in computer vision [26]. For one-dimensional spectral data, using a one-dimensional CNN (1D-CNN) is more adaptable in terms of dimensionality, and the 1D-CNN structure is more compact, requiring lower hardware resources. This allows for better fitting with fewer sample requirements during training [27]. [PERSON] et al. proposed a shallow CNN-based model for analyzing mixed gas infrared spectra, achieving precise concentration measurements of individual gas components [28]. [PERSON] et al. introduced a hierarchical feature fusion CNN model, which effectively reduces spectral cross-interference in SO\({}_{2}\)-NO\({}_{2}\)-NH\({}_{3}\) mixtures and improves the accuracy of gas concentration inversion [29]. [PERSON] et al. proposed a dual CNN (D-CNN) method for spectral signal denoising and concentration inversion, achieving the accurate measurement of CH\({}_{4}\)-N\({}_{2}\)O mixed gas concentrations [30]. However, the 1D-CNN model still has shortcomings. Firstly, these conventional 1D-CNN models use a single kernel size for each convolutional layer, limiting the richness of the features that can be extracted, resulting in the ineffective extraction and utilization of relevant information. Secondly, each channel updates weights in the same manner, neglecting the importance differences between different feature channels, leading to the extraction of redundant and interfering features, which reduces the inversion accuracy of the model. To address the issue of poor spectral consistency and the difficulty in achieving high-precision concentration inversion of the target gas due to the influence of external environmental factors on spectra collected by IFTS instruments, this paper proposes a multi-scale feature attention (MDISE) model. The model employs a multi-scale dilated convolution (MD) module to retain both global and local shallow features of the spectra; introduces the one-dimensional Inception (1D Inception) module to further extract multi-scale deep features; and incorporates the channel attention (SE) module to enhance attention to important spectral wavelengths, thereby suppressing redundant and interfering information.