Wavelet　transform　(WT)　is　an　advanced　preprocessing　technique,　which　has　been　widely　used　in　PM　10　prediction.　However,　this　technique　cannot　provide　stable　performance　due　to　the　empirical　selection　of　wavelet＇s　layers.　For　fixing　the　optimal　wavelet＇s　layers　in　PM10　forecasting,　an　innovative　coupled　model　based　on　WT,　long　short-term　memory　(LSTM),　and　SAE　(stacked　autoencoder)　are　proposed.　This　study　designs　a　crossover　experiment　with　960　high-　and　low-frequency　components　by　wavelet　decomposition　and　predicts　each　component　with　SAE-LSTM　based　on　12　samples　from　different　regions.　The　results　indicate　that　the　developed　model　outperforms　other　BiLSTM　(Biredictional　LSTM)　and　LSTM　based　on　some　error　evaluation　indicators　(i.e.　Nash-Sutcliffe　efficiency　coefficient　(NSEC)),　and　compared　with　other　steps,　the　accuracy　of　two-step　prediction　is　the　highest　in　view　of　root　mean　squares　error　(RMSE).　In　addition,　for　12　samples,　the　prediction　accuracy　by　using　high　layers　is　higher　than　that　by　adopting　low　layers　for　decomposing　them.　This　paper　fixes　the　optimal　wavelet＇　layers　in　PM10　prediction,　which　provides　a　meaningful　reference　in　other　prediction　scenarios　based　on　the　application　of　WT.