Simultaneous　localization　and　mapping　(SLAM)　is　mainly　used　to　solve　the　problem　of　mobile　robot　navigation　in　an　unknown　environment.　The　loop　closure　detection　is　a　key　step　in　visual　SLAM　and　plays　an　important　role　in　building　consistent　maps　and　reducing　pose　cumulative　errors.　A　new　loop　closure　detection　method　based　on　the　bag　of　semantic　word　(BOSW)　is　proposed　in　this　paper,　extracting　semantic　descriptors　by　using　a　neural　network　pre-trained　on　the　scene　recognition　data　set.　Traditionally,　the　visual　bag　of　word　(BoW)　model　adopts　the　features　such　as　ORB　and　SURF　which　is　easily　affected　by　the　environment.　With　advantages　of　better　ability　to　deal　with　changes　in　lighting,　the　convolutional　neural　network　can　extract　the　abstract　features　of　the　image.　For　these　reasons,　we　proposed　a　new　algorithm　that　combines　the　bag　of　word　model　and　the　convolutional　neural　network.　The　feature　maps,　extracted　by　the　VGG16　neural　network　which　is　trained　on　the　dataset　named　Places365　(VGG16-Places365),　are　used　as　the　semantic　descriptors　instead　of　ORB　descriptors.　The　workflow　of　building　the　vocabulary　of　the　semantic　words,　extracting　the　feature　vector　of　the　image　and　computing　the　similarity　score　is　presented.　The　performance　of　BOSW　is　evaluated　by　a　comparison　study　with　the　traditional　visual　bag　of　word　model　and　Inception-based　deep　learning　method　which　use　Inception＇s　fully　connected　layer　output　as　the　feature　vector　of　the　picture.　Overall,　the　experiments　on　New　College　datasets　verified　that　our　mode　show　stronger　generalization　and　can　achieve　higher　accuracy　in　the　loop　closure　detection.　©　2020　IEEE.