Indoor positioning based on ZigBee received signal strength index has attracted more and more researchers' attention and use because of its low cost, low hardware power consumption and easy implementation. Due to the influence of multipath effect and shadow effect, the traditional indoor positioning algorithm can not obtain good positioning effect. In order to improve the positioning accuracy of traditional wireless sensor network indoor positioning algorithm, this paper proposes an indoor positioning algorithm based on annealing algorithm (SA) and genetic algorithm (GA) optimized neural network (saga-bp). The initial weight and initial threshold of neural network algorithm are optimized by using annealing algorithm mechanism combined with genetic algorithm. The simulation results show that: the average positioning error of saga-bp algorithm is 0.40 m and the maximum positioning error is 0.83 m by adding a certain amount of random noise in the simulation; compared with the neural network (BP) positioning algorithm and genetic algorithm, the positioning accuracy of improved neural network (GA-BP) positioning algorithm is improved by 56% and 8.6%, which effectively improves the indoor positioning accuracy.