In view of the problems that indoor positioning systems based on Bluetooth low energy technology (BLE) use machine learning algorithms such as multi-layer perceptron (MLP) as positioning algorithm, which leads to the problem of poor positioning accuracy. An indoor positioning method based on long short-term memory network (LSTM) is proposed in this article, which uses the time domain information in the positioning process to improve the positioning accuracy. First of all, a fingerprint database is built by collecting received signal strength indication (RSSI), and then rely on the mapping relationship between RSSI and two-dimensional coordinates for network model training to obtain weight coefficients. Finally, use the trained neural network model to build an indoor positioning system. The test results show that the average positioning error of this system is 1.41m, improved by 49% and 16% respectively when it is compared with MLP and RNN algorithms, and the positioning accuracy is significantly improved, which can meet the needs of indoor positioning.