Simultaneous　localization　and　mapping　is　the　basis　for　solving　the　problem　of　robotic　autonomous　movement.　Loop　closure　detection　is　vital　for　visual　simultaneous　localization　and　mapping.　Correct　detection　of　closed　loops　can　effectively　reduce　the　accumulation　error　of　the　robot　poses,　which　plays　an　important　role　in　building　a　globally　consistent　environment　map.　Traditional　loop　closure　detection　adopts　the　method　of　extracting　handcrafted　image　features,　which　are　sensitive　to　dynamic　environments　and　are　poor　in　robustness.　In　this　paper,　a　method　called　stacked　convolutional　and　autoencoder　neural　networks　is　proposed　to　automatically　extract　image　features　and　perform　dimensionality　reduction　processing.　These　features　have　multiple　invariances　in　image　transformation.　Therefore,　this　method　is　robust　to　environmental　changes.　Experiments　on　public　datasets　show　that　the　proposed　method　is　superior　to　traditional　methods　in　terms　of　accuracy,　recall,　and　average　accuracy,　thereby　validating　the　effectiveness　of　the　proposed　method.