The　proliferation　of　fake　reviews　has　notably　impacted　consumer　decision-making.　However,　due　to　the　diversity　and　subtlety　of　fake　reviews,　traditional　sequential　models　face　challenges　in　capturing　relative　positional　information　and　suffering　from　the　loss　of　critical　information　of　long　texts.　Therefore,　we　propose　PosAtt-BiLSTM,　a　Bidirectional　Long　Short-Term　Memory　Neural　Network　(BiLSTM)　model　with　a　Hybrid　Attention　Mechanism　(HAM)　based　on　the　Non-Negative　Sinusoidal　Positional　Encoding　(NN-SPE)　for　fake　review　detection.　Firstly,　NN-SPE　modifies　the　traditional　sinusoidal　positional　encoding　by　adjusting　the　sine　and　cosine　functions　to　ensure　that　the　encoding　values　are　non-negative,　and　this　positional　information　is　then　merged　with　the　word　embeddings.　Then,　the　sequence　model　BiLSTM　is　employed　to　extract　sequential　features　from　the　text,　while　HAM　is　applied　to　effectively　weight　both　global　and　local　features,　ensuring　the　accurate　capture　and　transmission　of　key　information.　Three　public　datasets　Spam,　Yelp　Hotel,　and　Yelp　Restaurant　are　selected.　The　classification　results　demonstrate　that　PosAtt-BiLSTM　model　outperforms　traditional　models　such　as　BiRNN　and　BiLSTM　across　all　datasets.　Furthermore,　the　results　of　different　positional　encoding　methods　reveal　that　NN-SPE　leads　to　superior　performance　than　other　positional　encoding　techniques.　©　The　Author(s)　2025.