Accurate prediction of air quality is of great significance to people's daily life,therefore,a predictive model based on quadratic decomposition and improved sand cat swarm optimization(ISCSO)to optimize the long short-term memory(LSTM)network was proposed.First of all,The PM₂s data was decomposed into multiple subsequences using complete ensemble empirical mode decomposition with adaptivenoise(CEEMDAN)algorithm,and the reconstructed sequence that are not satisfied with the prediction effect was quadratically decomposed by variational mode decomposition (VMD)method.Secondly,the sand cat swarm optimization was improved by introducing Cubic chaotic,spiral search strategy and sparrow alert mechanism to improve the global search performance and convergence speed of the algorithm. Finally,a improved sand cat swarm algorithm was used to optimize the LSTM model parameters,the individual subsequences were input into the ISCSO-LSTM model for prediction and superimposed to obtain the final prediction results.The experimental results show that the CEEMDAN-VMD-ISCSO-LSTM combination model exhibits lower prediction errors,compared to the CEEMDAN-VMD-LSTM and CEEMDAN-VMD-SCSO-LSTM,the model proposed in this article has a 2.21 and 1.04 μg/m³reduction respectively in root mean square error,and has a 4.9%and 2.1% higher respectively in term of fit.