Existing　data　driven　low-cycle　fatigue　(LCF)　life　prediction　models　exhibit　limited　attention　to　the　heterogeneity　of　input　features　and　relatively　insufficient　consideration　of　cyclic　loading　condition.　This　study　introduces　a　novel　approach　based　on　a　long　short-term　memory　parallel　hierarchical　neural　network　(LSTM-PHNN).　Firstly,　the　input　features　are　classified　into　three　categories　based　on　their　physical　significance　and　distribution　characteristics:　elemental　parameters,　microstructural　property　parameters,　and　loading　parameters.　Next,　waveform　features　over　a　complete　cycle　are　reconstructed　for　different　loading　parameters　to　characterize　the　impact　of　time-series　cyclic　loading　condition　on　fatigue　life.　Finally,　three　predictive　models　were　developed:　a　fully　connected　neural　network　(FCNN),　a　parallel　hierarchical　neural　network　(PHNN),　and　the　proposed　LSTMPHNN　model.　Comparative　analysis　was　conducted　using　the　small　sample　LCF　dataset　of　316L　stainless　steel.　The　results　show　that　the　proposed　LSTM-PHNN　model　outperforms　both　the　FCNN　and　PHNN　models,with　almost　all　predictive　data　falling　within　a　scatter　band　of　1.5　times　and　the　prediction　accuracy　on　the　test　set　reaching　0.954.　The　above　demonstrate　that　the　LSTM-PHNN　model　provides　a　highly　accurate　and　robust　method　for　predicting　LCF　life　with　a　small　amount　of　experimental　data.