In fingerprint indoor positioning, constructing a highquality fingerprint database is a prerequisite for achieving highprecision positioning. Collecting enough signal samples at each reference point during the fingerprint dataset establishment stage usually consumes a lot of manpower and time costs, to solve this problem, this paper proposes a fingerprint database augmentation method based on an improved conditional deep convolutional generative adversarial network. The network model uses the reference point index as conditional information to generate corresponding samples for each reference point. It uses the least squares loss function instead of the cross-entropy loss function to avoid the problem of gradient disappearance that often occurs during training. Experimental results demonstrate that this method can effectively increase the sample size of each reference point, improve the training effect of the convolutional neural network and the positioning accuracy in small sample cases. The root mean square error is reduced to 0.44 meters, and the proportion of positioning errors within 1 meter is 86.98%, while that within 2 meters is 92.72%.