Cloud-layer identification is a key prerequisite for automated ground-based lidar processing, but remains challenging in Raman lidar networks because of incomplete near-range overlap, weak high-level cloud echoes, day–night signal-to-noise ratio (SNR) contrasts, energy fluctuations, and aerosol–cloud ambiguity. Leveraging the China Aerosol Raman Lidar NETwork (CARLNET), we propose a multi-channel cloud-layer identification framework that operates on 355/532/1064 nm elastic signals and 355/532 nm volume depolarization ratio, reducing dependence on absolute calibration and retrieved optical products. The framework combines an anisotropic encoder–decoder architecture with a three-stage training strategy, including self-supervised pretraining, traditional-algorithm-guided probabilistic distillation, and fine-tuning with limited expert refinement. Strict cross-site and cross-time evaluation on held-out sites shows that, without using any test-site labels, the framework achieves an F1 score of 0.9371 and reduces the mean absolute errors of cloud-base and cloud-top heights to 113 m and 213 m, respectively, outperforming training without pretraining by approximately 50 m and 70 m and the conventional baseline for cloud-top height by about 400 m. A labeled-data-size ablation further shows improved label efficiency, with near-plateau performance reached at about 40 labeled days. Consistency checks against co-located radiosonde moist-layer indications support the realism of identified high-level weak-echo clouds and the robustness of cross-site deployment. These results demonstrate a transferable and calibration-decoupled cloud-layer identification technique for network-scale Raman lidar processing.