Few-shot　object　detection　(FSOD)　aims　to　achieve　excellent　novel　category　object　detection　accuracy　with　few　samples.　Most　existing　researches　address　this　problem　by　fine-tuning　Faster　R-CNN,　where　the　model　is　first　trained　on　the　base　class　set　with　abundant　samples,　and　then　fine-tuned　on　the　novel　class　set　with　scarce　samples.　But　in　the　fine-tuning　stage,　the　connection　between　the　base　class　set　and　the　novel　class　set　is　ignored,　which　makes　it　difficult　to　learn　novel　classes　with　scarce　samples.　To　solve　this　issue,　we　propose　a　latent　knowledge-based　FSOD　method,　which　aims　to　utilize　latent　knowledge　to　build　connections　between　categories.　Specifically,　first　we　propose　a　latent　knowledge　classifier　(LK-Classifier),　which　realizes　object　recognition　by　splitting　features　through　latent　knowledge.　Then　a　guidance　module　is　designed　to　constrain　latent　knowledge　with　semantic　expression,　so　as　to　realize　the　bridge　between　base　class　set　and　novel　class　set　through　latent　knowledge.　Experimental　results　show　that　our　method　achieves　promising　results　on　the　FSOD　task　on　the　PASCAL　VOC　and　COCO　datasets,　especially　when　the　number　of　samples　is　extremely　scarce.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG　2022.