Deducing　the　twisting　pattern　of　spreader　is　very　important　for　crane　operators　in　steel　warehouse,　since　crane　spreaders　have　to　be　rotated　repeatedly　at　steel　bar　transport　sites.　To　improve　the　efficiency　of　the　steel　bar　delivery,　the　state　prediction　for　crane　spreaders　is　necessary.　In　order　to　fulfill　this　requirement,　the　HTSK-LN-ReLU　model　prediction　method　based　on　Optuna　optimization　is　applied　in　this　paper.　We　deploy　the　IMU　on　the　spreader　surface,　and　then　obtain　the　state　data　during　the　operation　of　the　crane　spreader　by　wireless　transmission.　The　data　are　first　denoised　through　fourier　filtering,　and　then　the　Optuna　framework　is　used　to　optimize　the　HTSK-LN-ReLU　model＇s　hyperparameters.　The　evaluation　index　of　mean　square　error　is　used　for　model　evaluation,　and　finally　the　optimal　hyperparameter　combination　of　the　model　is　selected　from　200　comparison　experiments.　Experimental　results　demonstrate　superior　performance　of　the　HTSK-LN-ReLU　model　in　predicting　the　state　of　crane　spreader　for　steel　bar　delivery.　©　2023　IEEE.