Purpose:　The　identification　of　early-stage　Parkinson＇s　disease　(PD)　is　important　for　the　effective　management　of　patients,　affecting　their　treatment　and　prognosis.　Recently,　structural　brain　networks　(SBNs)　have　been　used　to　diagnose　PD.　However,　how　to　mine　abnormal　patterns　from　high-dimensional　SBNs　has　been　a　challenge　due　to　the　complex　topology　of　the　brain.　Meanwhile,　the　existing　prediction　mechanisms　of　deep　learning　models　are　often　complicated,　and　it　is　difficult　to　extract　effective　interpretations.　In　addition,　most　works　only　focus　on　the　classification　of　imaging　and　ignore　clinical　scores　in　practical　applications,　which　limits　the　ability　of　the　model.　Inspired　by　the　regional　modularity　of　SBNs,　we　adopted　graph　learning　from　the　perspective　of　node　clustering　to　construct　an　interpretable　framework　for　PD　classification.Methods:　In　this　study,　a　multi-task　graph　structure　learning　framework　based　on　node　clustering　(MNC-Net)　is　proposed　for　the　early　diagnosis　of　PD.　Specifically,　we　modeled　complex　SBNs　into　modular　graphs　that　facilitated　the　representation　learning　of　abnormal　patterns.　Traditional　graph　neural　networks　are　optimized　through　graph　structure　learning　based　on　node　clustering,　which　identifies　potentially　abnormal　brain　regions　and　reduces　the　impact　of　irrelevant　noise.　Furthermore,　we　employed　a　regression　task　to　link　clinical　scores　to　disease　classification,　and　incorporated　latent　domain　information　into　model　training　through　multi-task　learning.Results:　We　validated　the　proposed　approach　on　the　Parkinsons　Progression　Markers　Initiative　dataset.　Exper-imental　results　showed　that　our　MNC-Net　effectively　separated　the　early-stage　PD　from　healthy　controls(HC)　with　an　accuracy　of　95.5%.　The　t-SNE　figures　have　showed　that　our　graph　structure　learning　method　can　capture　more　efficient　and　discriminatory　features.　Furthermore,　node　clustering　parameters　were　used　as　important　weights　to　extract　salient　task-related　brain　regions(ROIs).　These　ROIs　are　involved　in　the　development　of　mood　disorders,　tremors,　imbalances　and　other　symptoms,　highlighting　the　importance　of　memory,　language　and　mild　motor　function　in　early　PD.　In　addition,　statistical　results　from　clinical　scores　confirmed　that　our　model　could　capture　abnormal　connectivity　that　was　significantly　different　between　PD　and　HC.　These　results　are　consistent　with　previous　studies,　demonstrating　the　interpretability　of　our　methods.