It is a simple and effective way to determine the key factors and trace the causes of pollution by analyzing and predicting the influencing factors of air quality. Aiming at the prediction accuracy of current air quality prediction methods is not high, and it is easy to fall into the local optimal value problem, a novel model is proposed, which is based on Cross Recurrence Quantification Analysis (CRQA) and Deep Belief Network-Extreme Learning Machine (DBN-ELM) air quality data prediction method. Firstly, CRQA is used to analyze the correlation degree among various factors affecting air quality and screen out the critical factors affecting air quality. Then, the main influencing factors of air quality obtained are input into the DBN-ELM model for prediction. Concretely, DBN is used to extract key features of main air quality factors, and ELM is used for nonlinear approximation of final air quality time series data. The experimental results show that in the air quality data set of the Beijing Olympic Sports Center, the RMSE value and R2 value of this model are 1.7759 and 0.9833 respectively, which are better than other models. Furthermore, the effectiveness of the proposed model is verified by scatterplot and quantile-quantile plot.