With　the　global　climate　change,　the　high-altitude　detection　is　more　and　more　important　in　the　climate　prediction.　Due　to　the　interference　of　the　measured　objects　and　the　measured　environment,　the　input　and　output　characteristic　curve　of　the　pressure　sensor　will　shift,　resulting　in　nonlinear　error.　Aiming　at　the　difficulty　of　nonlinear　correction　of　pressure　sensor　and　the　low　accuracy　of　correction　results,　depth　neural　network　model　was　established　based　on　wavelet　function,　and　Levenberg-Marquardt　algorithm　is　used　to　update　network　parameters　to　realize　the　nonlinear　correction　of　pressure　sensor.　The　experimental　results　show　that　compared　with　the　traditional　neural　network　model,　the　improved　depth　neural　network　not　only　accelerates　the　convergence　rate,　but　also　improves　the　correction　accuracy,　meets　the　error　requirements　of　upper-air　detection,　and　has　a　good　generalization　ability,　which　can　be　extended　to　the　nonlinear　correction　of　similar　sensors.　©　2020,　Springer　Nature　Singapore　Pte　Ltd.