Indoor location technology based on WiFi fingerprint database has attracted much attention because of its high precision and easy deployment, while the quality of offline fingerprint database is a key factor to determine the location accuracy. To solve the problem of high acquisition cost of offline fingerprint database, a denoising fingerprint database enhancement model (FASRGAN-DAE) based on denoising autoencoder super resolution generation adductive network is proposed. The method enhances the location accuracy by enhancing the sparse fingerprint database. Specifically, firstly, the fingerprint data is mapped to the corresponding fingerprint image; then, on the basis of deleting the batch normalization layer (BN layer), the generator network improves the perception loss function to generate high-resolution fingerprint images, and reduces the hidden layer and output layer of the autoencoder to improve the quality of the generated images. Meanwhile, in the discriminator network, the BN layer is deleted and the output of the convolutional layer is used as the authenticity score of the input image. The mean square error loss function is used to optimize the discriminator network to enhance the ability of distinguishing between real and generated images. Finally, the fingerprint image is restored to the fingerprint data through the mapping module to realize the enhancement of the fingerprint database. Through the localization experiment in the real underground parking lot environment, compared with the original fingerprint database, the average localization error was reduced by 5.69% after FASRGAN-DAE enhanced data.