Loop　closure　detection　plays　a　vital　role　in　visual　simultaneous　localization　and　mapping　(SLAM),　since　it　can　reduce　the　accumulated　errors.　Handcrafted　feature-based　methods　for　loop　closure　detection　have　the　weakness　of　lack　of　robustness　with　respect　to　illumination　and　scale　changes.　In　recent　years,　the　Convolutional　Neural　Networks　(CNN)　has　been　widely　used　in　image　recognition　due　to　its　feature　expressive　capacity.　However,　these　deep　networks　are　too　complex　to　satisfy　real-time　for　loop　closure　detection.　In　this　paper,　we　propose　a　novel　simplified　convolutional　neural　network　(SCNN)　for　loop　closure　detection　in　visual　SLAM.　We　first　perform　superpixel　processing　on　the　original　image　to　reduce　the　effects　of　illumination　changes.　In　order　to　reduce　the　size　of　the　parameters,　we　invert　the　feature　maps　obtained　by　convoluting　and　concatenate　them　with　the　original.　In　addition,　we　pre-train　the　proposed　network　on　Places　dataset　to　solve　the　problem　of　the　scene　being　unlabeled.　Experimental　results　over　CityCenter　and　NewCollege　dataset　show　that　our　proposed　method　can　achieve　better　performance　counterparts　than　other.