As　sequential　recommendation　mainly　obtains　the　user　preference　by　analyzing　their　transactional　behavior　patterns　to　recommend　the　next　item,　how　to　mine　real　preference　from　user’s　sequential　behavior　is　crucial　in　sequential　recommendation,　and　how　to　find　the　user　long-term　and　short-term　preference　accurately　is　the　key　to　solve　this　problem.　Existing　models　mainly　consider　either　the　user　short-term　preference　or　long-term　preference,　or　the　relationship　between　items　in　one　session,　ignoring　the　complex　item　relationships　between　different　sessions.　As　a　result,　they　may　not　adequately　reflect　the　user　preference.　To　this　end,　in　this　paper,　a　Long-　and　Short-Term　Preference　Network　(LSPN)　based　on　graph　embedding　for　sequential　recommendation　is　proposed.　Specifically,　item　embedding　with　a　complex　relationship　of　items　between　different　sessions　is　obtained　based　on　graph　embedding.　Then　this　paper　constructs　the　network　to　obtain　the　user　long-　and　short-term　preferences　separately,　combing　them　through　the　fuzzy　gate　mechanism　to　provide　the　user　final　preference.　Furthermore,　the　results　of　experiments　on　two　datasets　demonstrate　the　efficiency　of　our　model　in　Recall@N　and　MRR@N.　©　Springer　Nature　Switzerland　AG　2020.