With　the　rapid　advancement　of　automation　and　intelligence　in　the　electronics　manufacturing　industry,　the　throughput　of　a　single　production　line　was　grown　exponentially.　Although　high　efficiency　brought　significant　cost　and　time　advantages,　it　also　led　to　two　major　challenges:　(1)　extremely　low　tolerance　for　error-any　slight　defect　might　have　caused　the　entire　product　to　be　scrapped;　(2)　increasingly　diverse　and　more　concealed　types　of　defects-bubble　defects,　internal　chip　defects,　printed　circuit　board　(PCB)　defects,　and　specific　process　defects　were　continuously　emerged,　posing　significant　challenges　to　the　inspection　process.　Traditional　manual　visual　inspection　or　single-task　deep　learning　models　were　often　struggled　to　balance　detection　efficiency　and　accuracy　in　complex　industrial　scenarios.　To　address　the　above　challenges,　a　single-stage　industrial　defect　detection　model　based　on　multi-dataset　mixed　training-MSAN-Net-was　proposed　in　this　paper.　Representative　datasets　covering　the　typical　scenarios　mentioned　above　were　collected　and　organized,　and　part　of　the　data　was　re-annotated　to　ensure　a　high　level　of　consistency　with　actual　production　environments.　MSAN-Net　was　adopted　an　integrated　architecture,　deeply　combining　UnifiedViT,　C2f　modules,　convolution　operations,　SPPF　structure,　and　Bi-Level　Routing　Attention　mechanism　to　achieve　accurate　identification　of　complex　industrial　defects.　Extensive　experiments　(including　comparisons　with　multiple　methods,　ablation　studies,　and　external　validation)　showed　that　MSAN-Net　was　outperformed　existing　SOTA　models　in　industrial　defect　detection　tasks,　significantly　improving　detection　accuracy　for　multi-class　defects　in　complex　scenarios,　reducing　reliance　on　manual　inspection,　and　effectively　lowering　scrap　losses　caused　by　defects,　thus　providing　a　reliable　solution　for　intelligent　quality　inspection　in　the　electronics　manufacturing　industry.