Background:　This　study　adopted　complete　meteorological　indicators,　including　eight　items,　to　explore　their　impact　on　hand,　foot,　and　mouth　disease　(HFMD)　in　Fuzhou,　and　predict　the　incidence　of　HFMD　through　the　long　short-term　memory　(LSTM)　neural　network　algorithm　of　artificial　intelligence.　Method:　A　distributed　lag　nonlinear　model　(DLNM)　was　used　to　analyse　the　influence　of　meteorological　factors　on　HFMD　in　Fuzhou　from　2010　to　2021.　Then,　the　numbers　of　HFMD　cases　in　2019,　2020　and　2021　were　predicted　using　the　LSTM　model　through　multifactor　single-step　and　multistep　rolling　methods.　The　root　mean　square　error　(RMSE),　mean　absolute　error　(MAE),　mean　absolute　percentage　error　(MAPE)　and　symmetric　mean　absolute　percentage　error　(SMAPE)　were　used　to　evaluate　the　accuracy　of　the　model　predictions.　Results:　Overall,　the　effect　of　daily　precipitation　on　HFMD　was　not　significant.　Low　(4 hPa)　and　high　(≥　21 hPa)　daily　air　pressure　difference　(PRSD)　and　low　(＜　7 °C)　and　high　(＞　12 °C)　daily　air　temperature　difference　(TEMD)　were　risk　factors　for　HFMD.　The　RMSE,　MAE,　MAPE　and　SMAPE　of　using　the　weekly　multifactor　data　to　predict　the　cases　of　HFMD　on　the　following　day,　from　2019　to　2021,　were　lower　than　those　of　using　the　daily　multifactor　data　to　predict　the　cases　of　HFMD　on　the　following　day.　In　particular,　the　RMSE,　MAE,　MAPE　and　SMAPE　of　using　weekly　multifactor　data　to　predict　the　following　week＇s　daily　average　cases　of　HFMD　were　much　lower,　and　similar　results　were　also　found　in　urban　and　rural　areas,　which　indicating　that　this　approach　was　more　accurate.　Conclusion:　This　study’s　LSTM　models　combined　with　meteorological　factors　(excluding　PRE)　can　be　used　to　accurately　predict　HFMD　in　Fuzhou,　especially　the　method　of　predicting　the　daily　average　cases　of　HFMD　in　the　following　week　using　weekly　multifactor　data.　©　2023,　The　Author(s).