In　the　social　media,　users　frequently　express　personal　emotions,　a　subset　of　which　may　indicate　potential　suicidal　tendencies.　The　implicit　and　varied　forms　of　expression　in　internet　language　complicate　accurate　and　rapid　identification　of　suicidal　intent　on　social　media,　thus　creating　challenges　for　timely　intervention　efforts.　The　development　of　deep　learning　models　for　suicide　risk　detection　is　a　promising　solution,　but　there　is　a　notable　lack　of　relevant　datasets,　especially　in　the　Chinese　context.　To　address　this　gap,　this　study　presents　a　Chinese　social　media　dataset　designed　for　fine-grained　suicide　risk　classification,　focusing　on　indicators　such　as　expressions　of　suicide　intent,　methods　of　suicide,　and　urgency　of　timing.　Seven　pre-trained　models　were　evaluated　in　two　tasks:　high　and　low　suicide　risk,　and　fine-grained　suicide　risk　classification　on　a　level　of　0　to　10.　In　our　experiments,　deep　learning　models　show　good　performance　in　distinguishing　between　high　and　low　suicide　risk,　with　the　best　model　achieving　an　F1　score　of　88.39%.　However,　the　results　for　fine-grained　suicide　risk　classification　were　still　unsatisfactory,　with　the　best　weighted　F1　score　of　50.89%.　To　address　the　issues　of　data　imbalance　and　limited　dataset　size,　we　investigated　both　traditional　and　advanced,　large　language　model　based　data　augmentation　techniques,　demonstrating　that　data　augmentation　can　enhance　this　model　performance　by　up　to　4.65%　points　in　F1-score.　Notably,　the　Chinese　MentalBERT　model,　which　was　pre-trained　on　psychological　domain　data,　shows　superior　performance　in　both　tasks.　This　study　provides　valuable　insights　for　automatic　identification　of　suicidal　individuals,　facilitating　timely　psychological　intervention　on　social　media　platforms.　The　source　code　and　data　are　publicly　available　at:　https://github.com/HongzhiQ/FineGrainedSuicideDetection.　©　2024　IEEE.