Wireless local area network (WLAN) based positioning plays an important role in smart homes, indoor navigation and userdefined services. Proposed a seq2seq model based WLAN indoor positioning method. The method is based on the seq2seq neural network model, which is widely adopted in the natural language processing (NLP). The seq2seq model can learn the relationships of the time sequences in the signal domain and the coordinate domain. After carefully designed signal pre-processing, sample augmentation and reasonable loss function, the learned model can be adopted for positioning. According to the experimental results from our collected data, our method can improve positioning accuracy compared with some other neural network based methods, including the RFSM method, the denoising autoencoder (DAE) based method and the f-RNN method, by 23%, 11% and 20% respectively.