Deep　learning-based　methods　have　advantages　for　general　Direction　of　Arrival　(DoA)　estimation　in　the　array　imperfections.　However,　existing　methods　using　regression　or　classification　are　challenged　to　balance　estimation　performance　and　complexity.　The　good　accuracy　and　adaptability　of　these　methods　is　achieved　by　constructing　complex　deep　models　as　well　as　large　datasets.　In　this　paper,　we　propose　a　novel　hierarchical　classification　framework　for　multi-DoA　estimation　with　coprime　array,　hoping　to　improve　the　accuracy　and　maintain　the　adaptation　based　on　an　appropriate　complexity　in　the　presence　of　array　sensor　location　errors.　Unlike　existing　data-driven　methods,　we　use　a　hierarchical　classifier　that　follows　the　idea　of　hierarchical　modeling　of　mapping　relationships.　This　makes　it　easier　to　learn　the　classification,　thereby　reducing　the　computational　burden.　The　DoA　estimation　process　is　divided　into　multi-level　according　to　the　concept　of　general　to　specific　direction.　The　complex　classification　task　can　then　be　divided　into　hierarchical　subtasks.　We　construct　a　tree　structure　as　priori　to　provide　hierarchical　relationships　between　labels.　By　learning　the　semantic　relationships　between　label　vectors,　our　proposed　hierarchical　model　will　provide　a　high-resolution　spatial　spectra.　Our　simulation　results　demonstrate　the　superiority　of　the　proposed　approach　over　the　existing　methods　in　accuracy,　adaptation,　and　complexity.　©　2025　Elsevier　Inc.