Diurnal variations in planetary boundary layer height (PBLH) is highly linked to weather, climate, and environmental processes. However, remaining challenges persist in estimating its diurnal behavior at a large scale due to insufficient observations and limitations of operational retrieval algorithms. This study proposed a deep learning framework based on an attention-augmented residual neural network to estimate diurnal variations in near-global PBLH, incorporating profiles from an non-sun-synchronous lidar (Cloud-Aerosol Transport System: CATS) and meteorological fields. The framework can largely address the issue of multi-layer structures in space-borne lidar signals, significantly improving the accuracy of PBLH retrieval during morning and evening (with accuracy improvement approach 40 % compared to traditional algorithms). Due to insufficient observations aligned with CATS orbits, a pre-trained model was firstly trained using pseudo-labels from reanalysis, and then was transferred to observation-based target labels. The transfer model demonstrated superior performance in most regions and periods, outperforming conventional algorithms in capturing PBLH magnitude and its diurnal variations, though under-performing over complex terrains. Further assessments over different land covers shown that the transfer-trained model estimated PBLH and diurnal patterns were highly consistent with those from radiosondes, surpassing reanalysis outputs. For model capability, wavelet covariance transformation derived potential PBLH and temperature profiles emerged as dominant factors, with contributions exhibiting diurnal patterns. Overall, this work proposes a novel framework for large-scale PBLH estimation and provides insights for improving conventional algorithms, particularly through integrating remote sensing and machine learning.