Generative　adversarial　networks　(GAN)　have　made　great　progress　in　areas,　such　as　computer　vision,　but　evaluation　of　GAN　models　is　still　a　daunting　task.　Numerous　researchers　have　proposed　many　methods　for　GAN　model　evaluation.　However,　most　methods　have　strong　pertinence,　which　leads　to　large　deviations　in　the　results　of　different　evaluation　methods.　To　date,　no　consensus　has　been　reached　on　which　method　best　captures　the　advantages　and　limitations　of　GAN　models.　In　this　study,　a　multi-method　fusion　approach　is　proposed　to　evaluate　GAN　models　from　many　aspects,　such　as　accuracy,　diversity　of　the　images　generated　by　the　model,　and　similarity　with　the　training　images.　This　method　provides　a　new　idea　for　GAN　model　assessment.　Meanwhile,　a　strategy　similar　to　ensemble　learning　is　used　to　analyze　the　generalization　of　multi-method　fusion　for　GAN　assessment.　This　method　and　the　human　eye　cognitive　assessment　model　are　used　on　celebA　benchmarks　and　self-acquired　image　data　sets　to　generate　results.　Through　human　eye　cognition　and　comparison　with　the　latest　GAN　model　assessment　method,　showing　that　the　multi-method　fusion　GAN　evaluation　method　proposed　in　this　paper　has　strong　robustness　and　effectiveness.　©　2019　IEEE.