As　an　improved　recurrent　neural　network,　Long　short-term　memory　(LSTM)　neural　network　have　been　widely　applied　in　many　areas.　However,　redundant　resources　of　calculation　and　memory　are　often　required　in　the　dense　LSTM　neural　network　and　the　over-fitting　problem　can　even　be　caused.　It　hinders　the　practical　application　of　network.　To　enhance　the　sparsity　and　generalization　ability,　the　sparse　LSTM　neural　network　with　hybrid　particle　swarm　optimization　algorithm　(SLSTM-HPSO)　is　proposed.　Firstly,　based　on　LSTM,　the　hybrid　coding　method　is　established　to　directly　denote　the　state　and　value　of　network　weights.　Secondly,　the　fitness　function　which　is　composed　of　the　network　training　error　and　1　norm　term　is　introduced　to　control　the　accuracy　and　sparsity　of　network,　simultaneously.　Thirdly,　the　update　strategy　is　designed　according　to　the　coding　method　and　fitness　function　to　search　for　optimal　solution.　Finally,　the　proposed　method　is　respectively　47.72%　and　47.02%　better　than　traditional　LSTM　neural　network　in　terms　of　prediction　accuracy　and　sparsity.　©　2021　IEEE