Objective　At　different　times,　public　health　faces　various　challenges　and　the　degree　of　intervention　measures　varies.　The　research　on　the　impact　and　prediction　of　meteorology　factors　on　influenza　is　increasing　gradually,　however,　there　is　currently　no　evidence　on　whether　its　research　results　are　affected　by　different　periods.　This　study　aims　to　provide　limited　evidence　to　reveal　this　issue.　Methods　Daily　data　on　influencing　factors　and　influenza　in　Xiamen　were　divided　into　three　parts:　overall　period　(phase　AB),　non-COVID-19　epidemic　period　(phase　A),　and　COVID-19　epidemic　period　(phase　B).　The　association　between　influencing　factors　and　influenza　was　analysed　using　generalized　additive　models　(GAMs).　The　excess　risk　(ER)　was　used　to　represent　the　percentage　change　in　influenza　as　the　interquartile　interval　(IQR)　of　meteorology　factors　increases.　The　7-day　average　daily　influenza　cases　were　predicted　using　the　combination　of　bi-directional　long　short　memory　(Bi-LSTM)　and　random　forest　(RF)　through　multi-step　rolling　input　of　the　daily　multifactor　values　of　the　previous　7-day.　Results　In　periods　A　and　AB,　air　temperature　below　22　degrees　C　was　a　risk　factor　for　influenza.　However,　in　phase　B,　temperature　showed　a　U-shaped　effect　on　it.　Relative　humidity　had　a　more　significant　cumulative　effect　on　influenza　in　phase　AB　than　in　phase　A　(peak:　accumulate　14d,　AB:　ER　=　281.54,　95%　CI　=　245.47　similar　to　321.37;　A:　ER　=　120.48,　95%　CI　=　100.37　similar　to　142.60).　Compared　to　other　age　groups,　children　aged　4-12　were　more　affected　by　pressure,　precipitation,　sunshine,　and　day　light,　while　those　aged　＞=　13　were　more　affected　by　the　accumulation　of　humidity　over　multiple　days.　The　accuracy　of　predicting　influenza　was　highest　in　phase　A　and　lowest　in　phase　B.　Conclusions　The　varying　degrees　of　intervention　measures　adopted　during　different　phases　led　to　significant　differences　in　the　impact　of　meteorology　factors　on　influenza　and　in　the　influenza　prediction.　In　association　studies　of　respiratory　infectious　diseases,　especially　influenza,　and　environmental　factors,　it　is　advisable　to　exclude　periods　with　more　external　interventions　to　reduce　interference　with　environmental　factors　and　influenza　related　research,　or　to　refine　the　model　to　accommodate　the　alterations　brought　about　by　intervention　measures.　In　addition,　the　RF-Bi-LSTM　model　has　good　predictive　performance　for　influenza.