The global demand rise in lithium, driven by the expansion of the new energy sector and electric vehicle markets, underscores the urgent need for efficient mineral processing of spodumene, the primary lithium ore. Traditional methods encounter challenges such as low-grade ores, fine particle sizes, complex mineral intergrowth, and high energy consumption with notable environmental impacts. This study addresses these issues by developing an intelligent X-ray Transmission (XRT) pre-sorting system enhanced with the novel Mineral-DeepLabV3+ deep learning algorithm. The approach integrates machine vision and artificial intelligence to deliver lightweight, precise ore segmentation under industrial mining conditions, incorporating innovations such as the MobileNetV4 backbone, optimized Atrous Spatial Pyramid Pooling module, and CSWin attention mechanism. Key findings demonstrate that the Mineral-DeepLabV3+ model achieves a mean intersection over union of 95.19 %, boosts segmentation accuracy by 3.28 %, and reduces parameter count by over 44 % compared to baseline models, while maintaining fast inference speed. Semi-industrial trials confirm its superior performance across varying ore sizes, processing rates, and conveyor speeds, achieving Li₂O concentrate grades up to 2.27% and waste rejection rates up to 66%. This technology significantly enhances resource efficiency, reduces environmental footprint, and advances operational sustainability in spodumene beneficiation. The proposed framework offers a scalable solution for driving low-carbon, intelligent practices in mineral processing.