Sequential　recommendation　aims　to　predict　future　user　interactions　by　analyzing　dynamic　patterns　within　their　historical　behavior　sequences.　Deep　neural　networks　have　recently　become　popular　for　learning　representations　of　these　sequences　in　the　time　domain.　However,　representing　users＇　intentions　in　the　time　domain　faces　challenges　such　as　noise　in　interactions　and　sparsity　of　data.　Contrastive　learning　and　representation　learning　in　the　frequency　domain　can　mitigate　these　issues　from　different　perspectives.　In　this　paper,　to　fully　integrate　timedomain　sequence　representations,　frequency-domain　sequence　representations　and　contrastive　learning　based　on　them,　we　propose　a　model　called　Time-Frequency　Consistency　based　contrastive　learning　for　Sequential　Recommendation　(TFCSRec).　TFCSRec　utilizes　a　time-domain　encoder　with　a　fully　connected　network　and　a　filter　network　to　extract　high-order　features　and　catch　pure　sequential　patterns.　Then,　a　learnable　frequencydomain　encoder　with　a　recurrent　neural　network　is　designed　to　capture　sequential　characteristics　in　the　frequency-domain　space.　Finally,　TFCSRec　combines　a　recommendation　task　and　two　contrastive　learning　tasks　to　optimize　the　two　user　representation　encoders.　Its　contrastive　learning　is　designed　to　minimize　a　contrastive　regularization　loss　and　a　time-frequency　consistency　loss,　which　for　the　first　time　is　constructed　directly　on　the　time-domain　sequence　representation　and　the　frequency-domain　sequence　representation.　Experiments　on　five　benchmark　datasets　show　that　the　proposed　TFCSRec　model　outperforms　other　sequential　recommendation　models　based　on　deep　neural　networks.