The　rapid　development　of　the　power　grid　makes　power　quality　disturbances　(PQDs)　more　complex.　Accurately　classifying　PQDs　and　measuring　the　duration　of　each　disturbance　element　are　both　crucial　for　studying　the　causes　of　PQDs　and　subsequent　countermeasures.　To　satisfy　the　requirements,　a　novel　dual-attention　optimization　model　(DAOM)　is　presented　in　this　paper　for　points　classification.　First,　the　local　feature　attention　mechanism　(LFAM)　based　on　Hilbert　transform　(HT)　is　proposed　in　the　input　layer　to　enhance　the　local　features　of　PQDs.　Subsequently,　on　the　basis　of　convolutional　neural　network　(CNN),　a　channel　attention　mechanism　(CAM)　based　on　squeeze-and-excitation　network　(SENet)　is　introduced　to　each　convolutional　layer　to　achieve　the　purpose　of　automatically　learning　the　importance　of　each　channel　feature.　Finally,　each　sampling　point　is　classified　in　the　form　of　multiclass-multioutput.　The　proposed　model　is　built　through　the　Keras　framework,　and　a　synthetic　database　containing　49　types　of　PQDs　is　established　to　test　the　model.　The　proposal　achieves　an　average　classification　accuracy　of　98.95%　in　a　30　dB　white　noise　environment,　which　is　more　precise　and　robust　than　other　deep　learning-based　models.　At　the　same　time,　the　proposed　model　consistently　performs　best　when　evaluated　through　imbalanced　data　classification　metrics　and　nonparametric　test　results.　Unlike　traditional　methods,　the　proposal　can　accurately　identify　the　occurrence　and　end　time　of　each　element　in　complex　PQDs.　Moreover,　through　a　hardware　experiment　based　on　the　AC　source,　the　proposed　model　achieves　an　average　accuracy　of　98.30%,　which　is　ahead　of　other　comparison　models　as　well.