By　establishing　a　database　of　urban　space　cases,　machine　learning　algorithms　and　deep　learning　algorithms　can　be　used　to　train　computers　to　learn　how　to　design　urban　spaces.　Based　on　the　basic　concepts　of　machine　learning　and　deep　learning　and　their　procedural　logic,　this　paper　explores　the　generation　mode　of　traffic　road　network,　neighborhood　space　form,　and　building　function　layout　of　urban　space　and　uses　the　northern　extension　of　the　central　green　axis　of　the　city　as　an　application　case　to　confirm　its　feasibility　in　order　to　seek　a　set　of　artificial　intelligence-based　urban　space　generation　design　method　and　provide　a　new　idea　for　the　innovative　development　of　urban　design　methods.