The　Parkinson’s　disease　gait　classification　method　proposed　in　this　paper　consists　of　five　steps.　Firstly,　gait　data　collection　includes　gait　videos　of　both　normal　individuals　and　Parkinson’s　patients　provided　by　the　Gait　Laboratory　of　Fujian　University　of　Traditional　Chinese　Medicine.　After　gait　data　collection,　preprocessing　of　the　data　is　conducted,　including　data　augmentation,　cropping,　resizing,　and　adding　dynamic　blur.　Next,　the　Attention-LSTM　model　is　constructed　to　effectively　capture　long-term　dependencies　in　time　series.　After　training　and　testing,　the　model　can　achieve　effective　classification　of　normal　individuals　and　Parkinson’s　patients　based　on　gait　video　data,　yielding　results.　The　experiments　demonstrate　that　the　model　constructed　in　this　paper　outperforms　baseline　models　and　previous　Parkinson’s　disease　classification　studies　based　on　video　data.　Our　research　reduces　the　difficulty　and　cost　of　gait　data　collection,　enabling　contactless　gait　analysis.　This　will　further　reduce　the　complexity　and　cost　of　Parkinson’s　disease　diagnosis　and　lay　a　solid　foundation　for　remote　diagnosis　of　Parkinson’s　disease.　©　ICST　Institute　for　Computer　Sciences,　Social　Informatics　and　Telecommunications　Engineering　2025.