Due to multipath effects and noise,the indoor visible light positioni ng which adopts the received signal strength (RSS) method is inaccurate in estimating the distance,resulting in low positioning accuracy.This paper proposes a distance estimation method based on BP neural network optimized by ge netic algorithm,which improves the positioning accuracy.Firstly,the initial weight of BP neural netw ork is optimized by genetic algorithm.The optimized BP neural network has fast convergence speed and is not easy to be limited to local optimum.The GA-BP neural network is then used to correct the distance between the transceiver terminals,so that the corrected distance is close to the real distance.Finally,the least squares method is used to solve the coordinates of the anchor points.At the same time,the GA-BP method is compared with the t raditional RSS weighted centroid method under different positioning ranges and different positioning locations.T he simulation results show that the average positioning error can reach 0.6428cm in the 5m×5m ×3m positioning area.Compared with the traditional RSS weighted centroid method,the average positioning accuracy improves by about 96.4%.Within different positioning ranges and locations,the average positioning error is stable at the millimeter level and will not expand with the expansion of the positioning area,which effectively improves indoor po sitioning accuracy and universal applicability of system applications.