In the broad domain of spectral analysis, the identification of present elements is a major task for qualitative analysis and a crucial preliminary for the following quantitative evaluation. Classic approaches require manual work with prior knowledge, which is time-consuming. To improve this process, neural-network-based methods have been introduced in the last decades. However, the scope of work is limited since usually only a small number of elements are covered. Compared to previous work, in this paper, we set up a new baseline by proposing a comprehensive framework capable of identifying the most common (up to 28) elements precisely and efficiently. Various neural network architectures are evaluated on large-scale simulation datasets and real measurements from the industry. Besides, to reduce the computational and data storage cost under big data industrial settings, our approach utilizes our previous work to select important features to reduce the data dimension while maintaining the prediction performance and interpretability. Compared to other dimension reduction baseline methods, our approach outperforms by achieving the best prediction accuracy and providing an intuitive data reduction result. Overall, results on real measurement data prove the feasibility of our approach as a general framework for large-scale spectral identification, and the application of the feature selection method can reduce 80% parameters and 96.9% FLOPs of CNN networks with even better test accuracy on real-world data.