Abstract: A channel state information (CSI) location algorithm based on the Gaussian mixture model and density based clustering (GMM-DBC) is proposed to deal with the low positioning accuracy and high time complexity of Bayesian indoor positioning technology. The GMM probability distribution model is constructed through the initial estimation of the parameters of the sub-model, and the errors were calculated. Then, introduce a strategy to determine the number of sub-models (DSM) to update the parameters of the GMM and reduce the localization error caused by the model accuracy. The tightness between reference points is judged on the basis of the distribution characteristics of different reference points, and the closely connected reference points are classified into one class to reduce the search scope and time complexity. The weights are calculated via the improved Bayesian probability algorithm according to the clustering results, so as to obtain the final positioning results. The experimental results show that the proposed algorithm can well improve the positioning accuracy and reduce the time complexity.