As　grasping　behaviors　in　real　packaging　scenarios　are　apt　to　be　influenced　by　various　disturbances,　visual　grasping　prediction　systems　have　suffered　from　the　poor　robustness　and　low　detection　accuracy.　In　this　study,　an　intelligent　robotic　grasp　framework　(RTnet)　underpinned　by　a　linear　global　attention　mechanism　has　been　proposed　to　achieve　the　highly　robust　robot　grasp　prediction　in　real　packaging　factory　scenarios.　First,　to　reduce　the　computational　resources,　an　optimized　linear　attention　mechanism　has　been　developed　in　the　robotic　grasping　process.　Then,　a　local　window　shifting　algorithm　has　been　adapted　to　collect　feature　information　and　then　integrate　global　features　through　the　hierarchical　design　of　up　and　down　sampling.　To　further　improve　the　developed　framework　with　the　capability　of　mitigating　noise　interference,　a　self-normalizing　feature　architecture　has　been　established　to　empower　its　robust　learning　capabilities.　Moreover,　a　grasping　dataset　in　the　real　operational　environment　(RealCornell)　has　been　generated　to　realize　a　transition　to　real　grasping　scenarios.　To　evaluate　the　performance　of　the　proposed　model,　its　grasp　prediction　has　been　experimentally　examined　on　the　Cornell　dataset,　the　RealCornell　dataset,　and　the　real　scenarios.　Results　have　shown　that　RTnet　has　achieved　a　maximum　accuracy　of　98.31%　on　the　Cornell　dataset　and　93.87%　on　complex　RealCornell　dataset.　Under　the　consideration　of　real　packaging　situations,　the　proposed　model　have　also　demonstrated　the　high　levels　of　accuracy　and　robustness　in　terms　of　grasping　detection.　Summarily,　RTnet　has　provided　a　valuable　insight　into　the　advanced　deployment　and　implementation　of　robotic　grasping　in　the　packaging　industry.