## 1 Introduction  
Gems are high-value and diverse minerals or rocks [1]. Effectively identifying gem species can prevent confusion between counterfeits, synthetic gems, and natural gems. Traditional gem identification methods mainly rely on manual observation, testing, and experience-based judgment, which are subjective, unstable, inaccurate, and inefficient. To overcome these issues, spectroscopic analysis technology, as a non-destructive, rapid, accurate, and objective identification method, has gradually gained widespread attention and application [2]. Spectral analysis is a method that uses the spectrum of a substance to identify its composition and relative chemical content. Ultraviolet-visible (UV-VIS) absorption spectroscopy determines the composition, structure, and properties of jade by analyzing the selective absorption characteristics of valence electrons within the material under electromagnetic radiation [3]. The position of absorption peaks and the intensity of absorption in the spectrum are used for qualitative and quantitative analysis of the components. Due to the different crystal structures and chemical compositions of gemstones, they exhibit distinct ultraviolet-visible absorption spectral features. UV-VIS absorption spectroscopy is also included as a common method for gem identification in the national standard GB/T 42645-2023 [3] \"Gem Identification--Ultraviolet-Visible Absorption Spectroscopy\". UV-VIS are highly efficient and enable rapid analysis, allowing for direct and non-destructive testing of samples [4]. However, due to the variety of gemstone types, ultraviolet spectra also show complex and diverse forms. It requires a comprehensive consideration of the wavelength position, number, shape, and relative intensity of absorption peaks, making it difficult to quickly and accurately describe and classify using subjective judgment. Gemstone identification based on ultraviolet spectroscopy mainly relies on chemometric models that characterize the relationship between spectral data and gemstone categories [5]. Traditional spectral analysis methods include Principal Component Analysis (PCA) [6; 7], Partial Least Squares Discriminant Analysis (PLS_DA), Support VectorMachines (SVM) [8], among others. Although the resolution of spectral measurement equipment is continuously improving, providing more effective information and reducing more noise, the high dimensionality and susceptibility to the interference of spectra, as well as unavoidable noise, pose challenges. This makes it difficult for traditional chemometrics to directly extract effective features from spectra. Therefore, how to use advanced computer technology to improve the accuracy and efficiency of jade ultraviolet spectral analysis, and quickly achieve jade identification, is a pressing problem that needs to be addressed. In recent years, deep-learning-based spectral analysis techniques have rapidly developed [9] and have been widely applied in fields such as agricultural products [10], pharmaceuticals [11], minerals, and medicine [12]. In 2017, [PERSON] et al. were the first to apply convolutional neural network (CNN)-based methods to classify ten different types of spectra [13]. Compared to traditional chemometric methods, CNNs achieved higher accuracy in quantitative analysis and were less dependent on spectral data processing. In 2019, another study [14] introduced the Inception structure [15] based on CNN, using combinations of convolution kernels of different sizes to extract multi-scale local features of the spectra, thus improving the adaptability of the network. CNN-based classification models [16; 17; 18] can effectively capture local features of spectra through convolutional layers, but the fixed size of the convolution kernels limits CNNs in learning long-range features. Spectral data, characterized by wavelength or frequency amplitudes, have properties similar to time-frequency sequences [19], and CNNs struggle to capture long-distance feature dependencies.