Suicide　and　suicidal　behaviors　remain　significant　challenges　for　public　policy　and　healthcare.　In　response,　psy-chological　support　hotlines　have　been　established　worldwide　to　provide　immediate　help　to　individuals　in　mental　crises.　The　effectiveness　of　these　hotlines　largely　depends　on　accurately　identifying　callers＇　emotional　states,　particularly　underlying　negative　emotions　indicative　of　increased　suicide　risk.　However,　the　high　demand　for　psychological　interventions　often　results　in　a　shortage　of　professional　operators,　highlighting　the　need　for　an　effective　speech　emotion　recognition　model.　This　model　would　automatically　detect　and　analyze　callers＇　emotions,　facil-itating　integration　into　hotline　services.　Additionally,　it　would　enable　large-scale　data　analysis　of　psychological　support　hotline　interactions　to　explore　psychological　phenomena　and　behaviors　across　populations.　Our　study　utilizes　data　from　the　Beijing　psychological　support　hotline,　the　largest　suicide　hotline　in　China.　We　analyzed　speech　data　from　105　callers　containing　20,630　segments　and　categorized　them　into　11　types　of　negative　emotions.　We　developed　a　negative　emotion　recognition　model　and　a　fine-grained　multi-label　classification　model　using　a　large-scale　pretrained　model.　Our　experiments　indicate　that　the　negative　emotion　recognition　model　achieves　a　maximum　F1-score　of　76.96%.　However,　it　shows　limited　efficacy　in　the　fine-grained　multi-label　classification　task,　with　the　best　model　achieving　only　a　41.74%　weighted　F1-score.　We　conducted　an　error　analysis　for　this　task,　discussed　potential　future　improvements,　and　considered　the　clinical　application　possibilities　of　our　study.　All　the　codes　are　public　available　at:　https://github.com/cz10914/psy_hotline_analysis.　©　2024　IEEE.