To address the issues of high fingerprint collection costs and the difficulty of constructing datasets in indoor positioning, a method for indoor positioning fingerprint generation based on a conditional sparse autoencoder generative adversarial network is proposed. This method enhances the feature extraction capability by adding hidden and output layers to the autoencoder, guiding the generator to learn and generate key features of fingerprint data. A fingerprint selection algorithm is used to filter out the most relevant fingerprint data, which is then added to the fingerprint database and used to train a convolutional long short-term memory network model for online performance evaluation. Experimental results show that the conditional sparse autoencoder generative adversarial network improves the accuracy of indoor positioning in multi-building, multi-floor environments without increasing the number of collected samples. Compared to the original conditional generative adversarial network model, the positioning error in predictions on the UJIIndoorLoc dataset is reduced by 6%, and in practical applications, the positioning error is reduced by 14%.