Long　short-term　memory　(LSTM)　neural　network　has　been　widely　studied　and　applied　in　the　real　world.　To　obtain　the　LSTM　neural　network　with　better　accuracy　and　more　appropriate　structure,　the　hybrid　coding　particle　swarm　optimization　(HCPSO)　algorithm　is　proposed.　Firstly,　the　hybrid　coding　scheme　is　developed　to　represent　the　weights　and　structure　of　LSTM　neural　network,　simultaneously.　Then,　the　novel　update　mechanism　is　proposed　to　adjust　the　position　of　particles.　Meanwhile,　the　discrete　update　strategy　(DUS)　and　adaptive　nonlinear　moderate　random　search　strategy　(ANMRS)　are　proposed　to　enhance　the　convergence　and　global　search　capability　of　HCPSO,　respectively.　Finally,　the　effectiveness　of　HCPSO　is　demonstrated　by　multiple　numerical　examples.　The　experiment　results　show　that　the　proposed　HCPSO　algorithm　is　more　competitive　in　optimizing　LSTM　neural　networks　than　other　algorithms.