In the indoor positioning, the traditional RFID positioning method cannot accurately estimate the current path loss coefficient with the change of indoor environment due to its simple method. It has disadvantages such as large environmental impact, low positioning accuracy, and poor real-time performance. In order to solve the above problems, this study puts forward a kind of indoor location algorithm based on dual neural network model, and establishes the BP network and the network within DNN dual neural network model. Then, it preprocesses the collected RSSI signal and inputs the preprocessed signal value to BP network model, outputs path loss coefficient n, and then received signal strength value RSSI and through the BP model to get the path loss coefficient of n as input, input to the network within DNN model, and get the precise positioning of the labels under test coordinates. Experiments show that compared with the traditional indoor positioning algorithm based on RSSI and ANN model, this algorithm effectively improves the positioning accuracy and real-time performance.