Deep　neural　networks　have　been　successfully　applied　to　the　classification　of　brain　networks.　However,　the　high-dimensional　and　small-scale　properties　of　the　brain　network　data　limit　their　extensive　applications.　To　solve　this　problem,　this　paper　proposes　a　new　deep　forest　framework　with　cross-shaped　window　scanning　mechanism　(DF-CWSM)　to　extract　topological　features　for　the　classification　of　brain　networks.　The　cross-shaped　window　scanning　mechanism　is　designed　to　extract　the　node-level　and　the　edge-level　features　respectively　that　have　meaningful　interpretations　in　terms　of　corresponding　network　topologies.　Based　on　the　classification　framework,　we　firstly　implement　the　feature　transformation　of　brain　networks　by　the　multi-level　topological　feature　extraction.　Then　a　cascade　forest　structure　is　used　to　learn　the　hierarchical　features　layer　by　layer.　And　the　results　of　the　last　level　of　cascade　forests　are　integrated　to　make　the　final　classification.　We　evaluated　the　proposed　framework　on　the　ABIDE　I　data　set.　Experimental　results　show　that　our　proposed　framework　can　not　only　achieve　competitive　classification　performance　but　also　accurately　identify　the　abnormal　brain　regions　associated　with　ASD.　©　2019　IEEE.