Unsupervised　sentence　embedding　methods　based　on　contrastive　learning　have　gained　attention　for　effectively　representing　sentences　in　natural　language　processing.　Retrieving　additional　samples　via　a　nearest-neighbor　approach　can　enhance　the　model＇s　ability　to　learn　relevant　semantics　and　distinguish　sentences.　However,　previous　related　research　mainly　focused　on　retrieving　neighboring　samples　within　a　single　batch　range　or　global　range,　which　makes　the　model　possibly　unable　to　capture　effective　semantic　information　or　incurs　excessive　time　cost.　Furthermore,　previous　methods　use　retrieved　neighbor　samples　as　hard　negatives.　We　argue　that　nearest　neighbor　samples　contain　relevant　semantic　information,　and　treating　them　as　hard　negatives　risks　losing　valuable　semantic　knowledge.　In　this　work,　we　introduce　Neighbor　Contrastive　learning　for　unsupervised　Sentence　Embeddings(NCSE),　which　combines　contrastive　learning　with　the　nearest-neighbor　approach.　Specifically,　we　create　a　candidate　set　to　store　sentence　embeddings　across　multiple　batches.　Retrieving　the　candidate　set　can　ensure　sufficient　samples,　making　it　easier　for　the　model　to　learn　relevant　semantics.　Using　retrieved　nearest　neighbor　samples　as　positives　and　applying　the　self-attention　mechanism　to　aggregate　the　sample　and　its　neighbors　encourages　the　model　to　learn　relevant　semantics　from　multiple　neighbors.　Experiments　on　the　semantic　text　similarity　task　demonstrate　our　method＇s　effectiveness　in　sentence　embedding　learning.