The　cascaded　H-bridge　inverter　exhibits　the　characteristics　of　high　voltage,　large　capacity　and　low　harmonic　distortion,　and　has　a　vital　impact　on　application　fields　such　as　battery　energy　storage　and　photovoltaic　power　generation.　Fault　diagnosis　of　inverter　switches　is　essential　to　enhancing　equipment　dependability.　However,　the　limited　number　of　fault　samples　and　severe　overlap　of　fault　signals　in　real-word　applications　present　difficulties　for　inverter　fault　diagnosis.　In　view　of　this,　this　paper　introduces　a　hierarchical　classification　fault　diagnosis　strategy　founded　on　an　improved　siamese　network　to　achieve　high-precision　fault　diagnosis.　Firstly,　for　the　purpose　of　addressing　the　issues　of　multiple　fault　categories　and　limited　samples,　an　improved　Siamese　network　based　on　long　short-term　memory　and　attention　mechanism　is　proposed　to　extract　more　subtle　fault　difference　features,　thereby　improving　the　recognition　accuracy　of　overlapping　fault　classes.　Then,　to　solve　the　problem　of　serious　overlap　of　fault　samples　of　different　types　in　the　preliminary　grouping,　a　hierarchical　fault　diagnosis　model　is　proposed　to　realize　high　precision　fault　diagnosis.　Finally,　the　fault　data　of　the　cascaded　H-bridge　inverter　was　obtained　through　the　semi-physical　simulation　platform　to　complete　the　diagnosis　experiment.　The　experimental　results　demonstrate　the　recommended　model　offers　clear　benefits　in　terms　of　diagnostic　accuracy　when　compared　to　the　conventional　model.　©　2025　Institute　of　Physics　Publishing.　All　rights　reserved.