Aiming at the problems of low accuracy of indoor positioning technology and computational complexity, an indoor fingerprint location algorithm based on optimized convolutional autoencoder-self organizing map (OCAE-SOM) is proposed. In the offline stage, first, we use the amplitude and phase-preprocessing matrix of a channel state information as the original input data and adjust it to the RGB format to train the convolutional autoencoder (CAE) algorithm so that it can deeply mine the fingerprint features of a reference point. The Adam algorithm is employed to optimize the parameters of the CAE algorithm, which not only reduces the data dimension but also improves training efficiency. Then, we use the OCAE-SOM algorithm for model training. It can shorten the time to train the model separately. Finally, we use the Adam algorithm to optimize the weight of the self-organizing map, which can be better retain the correlation between output features to avoid the local optimization of weight parameters. In the online stage, the adjusted test data are input into the OCAE-SOM algorithm, and the output location point is obtained after matching. The experimental results show that the OCAE-SOM algorithm is significantly better than existing algorithms in terms of positioning time and accuracy, and it has certain application values.