The Terrestrial Ecosystem Carbon Inventory Satellite （TECIS/CM-1） utilizes a combination of multi-beam lidar， multi-spectral cameras， and other passive and active sensors for synergistic observations， enabling high-resolution， comprehensive， and three-dimensional atmospheric monitoring of clouds and aerosols. In recent years， traditional algorithms have faced challenges in terms of vertical layer retrieval accuracy and robustness in complex environments with low signal-to-noise ratios， near-surface observations， and mixed multi-layer structures. To address these issues， this paper proposes TECIS-CASNet， a generalized framework for atmospheric layer recognition and application， designed for the novel multi-beam lidar on the TECIS， leveraging the characteristics of the lidar data and deep learning attention mechanisms. To validate the reliability of this framework， the research team conducted multiple ground-based synchronous observation experiments to systematically evaluate its recognition accuracy. Finally， as a demonstrative application， the study focuses on a typical long-distance dust transport event in the Beijing-Tianjin-Hebei region of China， showcasing the practical application value of the framework. The results indicate that the TECIS-CASNet framework achieves high cloud-aerosol recognition accuracy， reaching 98.41%， and is capable of reducing misidentification and missed detection in complex environments， including low signal-to-noise ratios， near-surface layers， and multi-layer mixed structures. The absolute accuracy of aerosol optical depth retrieval is 0.01， with an overall accuracy of 98%. This paper， centered around the TECIS-CASNet framework， provides significant insights for lidar satellite atmospheric remote sensing data processing and environmental monitoring applications.