The　entorhinal-hippocampus　structure　in　the　mammalian　brain　is　the　core　area　for　realizing　spatial　cognition.　However,　the　visual　perception　and　loop　detection　methods　in　the　current　biomimetic　robot　navigation　model　still　rely　on　traditional　visual　SLAM　schemes　and　lack　the　process　of　exploring　and　applying　biological　visual　methods.　Based　on　this,　we　propose　a　map　construction　method　that　mimics　the　entorhinal-hippocampal　cognitive　mechanism　of　the　rat　brain　according　to　the　response　of　entorhinal　cortex　neurons　to　eye　saccades　in　recent　related　studies.　That　is,　when　mammals　are　free　to　watch　the　scene,　the　entorhinal　cortex　neurons　will　encode　the　saccade　position　of　the　eyeball　to　realize　the　episodic　memory　function.　The　characteristics　of　this　model　are　as　follows:　1)　A　scene　memory　algorithm　that　relies　on　visual　saccade　vectors　is　constructed　to　imitate　the　biological　brain＇s　memory　of　environmental　situation　information　matches　the　current　scene　information　with　the　memory;　2)　According　to　the　information　transmission　mechanism　formed　by　the　hippocampus　and　the　activation　theory　of　spatial　cells,　a　localization　model　based　on　the　grid　cells　of　the　entorhinal　cortex　and　the　place　cells　of　the　hippocampus　was　constructed;　3)　Finally,　the　scene　memory　algorithm　is　used　to　correct　the　errors　of　the　positioning　model　and　complete　the　process　of　constructing　the　cognitive　map.　The　model　was　subjected　to　simulation　experiments　on　publicly　available　datasets　and　physical　experiments　using　a　mobile　robot　platform　to　verify　the　environmental　adaptability　and　robustness　of　the　algorithm.　The　algorithm　will　provide　a　basis　for　further　research　into　bionic　robot　navigation.