Aiming at the problems of poor prediction accuracy and unstable prediction results of BP neural network, an improved slime mold algorithm (ISMA) is proposed to optimize the prediction model of BP neural network, and Tent chaotic mapping is introduced to overcome the shortcomings of uneven initial population distribution. The leader strategy and Levy flight strategy are introduced to solve the randomness of the position update and the problem of falling into local optimality. The adaptive reverse learning strategy is used to expand the search space and 23 groups of benchmark functions are tested. Then the ISMA algorithm was used to optimize the initial weights and thresholds of the BP network model, and the ISMA-BP Air quality index prediction model was constructed. At last, 779 sets of AQI data were collected and put into the prediction model for testing and analysis. The experimental results showed that, Compared with BP neural network model, GWO-BP model and SMA-BP model, ISMA-BP model has higher accuracy in predicting AQI. The mean square error of ISMA-BP model is 3.840 2, and the mean absolute error is 1.507 8 respectively.