Aimed at the problems such as slow convergence speed, low accuracy and easy to fall into local optimality, an improved sparrow search algorithm based on multiple strategies（ISSAMS） is proposed in this paper. Circle chaotic mapping was introduced to initialize the population, increase the diversity of the population, and improve the global search ability. Using sine and cosine search strategy was used to update the location of the finder, select the best location, enhance the local search ability, and avoid falling into the global optimization. Firefly disturbance was added to update the optimal individual position, search for feasible solutions, and improve the local search ability and search speed. In order to verify the effectiveness of the improved algorithm, five reference functions were selected for simulation experiments, among which three functions were single-peak functions and two functions were multi-peak functions, and compared with genetic algorithm, grey wolf algorithm, particle swarm optimization algorithm and sparrow search algorithm. The simulation results show that the improved sparrow search algorithm based on multiple strategies has the ability to jump out of the local optimal solution, the convergence speed is faster, the accuracy is higher, and the overall performance is better than the other four algorithms. By applying this improvement to the threshold and weight of the optimized BP neural network, the error of the BP model optimized by no improved sparrow search algorithm is reduced by 14. 73%. Compared with the BP model optimized by the other three algorithms, the average absolute percentage error of the optimized model based on the multi-strategy improved sparrow search algorithm is the lowest and has the best effect. The effectiveness of the improved algorithm is further verified. [ABSTRACT FROM AUTHOR]