Indoor positioning plays a crucial role in enabling location-based services in sensor networks that cannot be reached by GNSS. In the wireless positioning system, the wireless fingerprint-based positioning method only needs to compare the signal of the device to be located with the known features to determine the location, which is widely used in indoor scenes because of its low complexity. However, due to the fading and multipath effects caused by the complex and changeable indoor environment, which will lead to the fluctuation of indoor signal values, thereby reducing the positioning accuracy, most of the current methods ignore the temporal and spatial information of fingerprint collection, in order to solve these problems, this paper proposes an algorithm model combining deep residual network (ResNet) and indoor fingerprint positioning. In order to solve the problem of random fluctuation of indoor signals, the particle filter can better adapt to the changes of the dynamic environment, and the self-attention mechanism algorithm is used to dynamically adjust the particle weight, so that the algorithm model proposed in this paper can better capture the signal features in the room, so as to improve the positioning accuracy and robustness. Finally, the corresponding experimental verification is carried out, and the experimental results show that the average positioning error of the SA-ResNet indoor positioning algorithm model fluctuates between 0.56 and 0.62 m, which has good stability.