The　main　content　of　this　study　is　the　human-machine　collaborative　design　research,　taking　the　car　body　design　as　the　carrier.　The　research　framework　focused　on　two　phases　of　car　body　design　process,　that　of　design　ideation　and　evaluation.　In　the　ideation　stage,　we　trained　an　imperfect　Deep　Convolutional　Generative　Adversarial　Network　(DCGAN)　model　that　just　could　generate　blur　automobile　images　as　the　blur　design　motherboards　for　the　iterative　sketching,　which　had　design　uncertainties　and　blanks,　thus　activating　designers＇　subjective　initiative　and　aesthetic　intuition　to　provide　more　creative　deepen　sketches.　We　leveraged　motherboards　to　address　uncertainty　through　sketching　and　aesthetic　intuition,　refining　options　and　ultimately　selecting　an　optimal　design.　In　the　evaluation　phase,　we　initially　constructed　a　parametric　3D　model　with　20　parameters　based　on　the　optimal　design,　and　invited　32　designers　conducting　participatory　design　experiments,　getting　1024　human-designed　schemes.　Following　this,　we　administered　an　online　survey　to　assess　the　aesthetic　qualities　of　a　total　of　1024　design　schemes.　Leveraging　the　collected　score　data　(The　first　round　of　surveys　engaged　279　participants,　while　the　second　round　involved　73　participants),　we　trained　an　Artificial　Neural　Network　(ANN)　model　to　serve　as　an　aesthetic　evaluation　score　predictor　for　unknown　parameter　configurations.　The　machine　could　evaluate　designs　autonomously,　thus　selecting　best　design　from　20,000　schemes　generated　randomly　by　machine.　We　utilized　the　parametric　design　converting　sketching　images　to　the　numeric　parameters,　switching　the　qualitative　ideation　to　the　quantitative　evaluation,　thus　achieving　aesthetic　evaluation　and　optimization.　This　study　explores　the　relationship　between　human　cognitive　intuition　and　machine　intelligence　and　how　they　can　collaborate　with　each　other.