Automated　model　generation　(AMG)　is　a　robust　algorithm　designed　to　expedite　the　development　of　artificial　neural　network　(ANN)　models,　particularly　widely　applied　in　microwave　modeling.　AMG　algorithm　encompasses　two　key　aspects,　i.e.,　data　sampling　and　model　structure　adaptation.　The　model　structure　adaptation　process　in　traditional　AMG　approaches　can　only　independently　adjust　the　number　of　hidden　layers　or　the　quantity　of　neurons　within　these　layers,　lacking　the　capability　to　simultaneously　adapt　both.　To　enhance　the　efficiency　of　ANN　modeling,　we　present　an　efficient　AMG　algorithm　utilizing　batch-adjustment　technique　for　adaptive　ANN　structure　modification.　This　algorithm　dynamically　adds　or　removes　hidden　layers　in　batches,　to　maintain　a　balance　between　the　number　of　layers　and　neurons.　In　comparison　to　traditional　AMG　algorithms,　the　ANN　structure　adaptation　of　the　AMG　using　the　batch-adjustment　algorithm　is　more　flexible　and　the　automated　modeling　process　of　microwave　components　is　more　efficient.　This　presented　AMG　algorithm　is　suitable　for　neural　network　modeling　of　various　microwave　components,　such　as　microwave　filters.　The　modeling　of　an　iris-coupled　cavity　filter　is　used　as　an　example　to　demonstrate　the　superior　performance　of　the　AMG　with　the　batch-adjustment　algorithm.　©　2024　IEEE.