Aiming at the disadvantages of traditional Bald Eagle Search optimization algorithm （BES）， such as easy to fall into the local optimum and slow convergence， a BES with Golden Sine Algorithm （Gold-SA） and crisscross strategy （GSCBES） was proposed. Firstly， the position update formula based on inertia weight was set in the traditional BES search stage. Then， Gold-SA was introduced in the stage of predation. Finally， the crisscross strategy was introduced to modify the global optimum and population. The optimization ability of the proposed algorithm was evaluated by the simulation experiments on 11 Benchmark functions， CEC2014 functions and by using Wilcoxon rank sum test. The results show that the proposed algorithm converges faster. At the same time， the weights and thresholds of Back Propagation （BP） neural network were assigned by the proposed algorithm， and the optimized BP neural network model was used in the prediction of air quality， the values of Mean Absolute Error （MAE）， Root Mean Square Error （RMSE）， Mean Square Error （MSE）， and Mean Absolute Percentage Error （MAPE） are smaller than those of BP neural network model and Particle Swarm Optimization （PSO） based BP neural network model，and the prediction accuracy is improved.