In order to improve the accuracy of indoor positioning and reduce the cost of onsite investigation, an indoor localization method based on Gaussian process regression and WiFi fingerprint is proposed. Firstly, during the offline phase, Gaussian process regression model (GPR) is used to expand the WiFi fingerprint database. In other words, by training different GPR kernel functions, the best GPR prediction model is obtained, and then the signal strength (RSS) of unknown region is predicted by using the limited known data. Then in the fingerprint matching stage, the weighted nearest neighbor algorithm (WKNN), maximum likelihood estimation (MLE) and multilayer perceptron (MLP) methods are used to locate the unknown points according to the RSS database obtained in the offline phase. Specifically, in order to further improve the positioning accuracy, an error correction model is proposed and applied to the above different positioning algorithms. The experimental results show that the kernel function combination of kRBF+kMatern+kRQ is the best GPR prediction model, and the average RSS estimation error is 459 dBm. In addition, compared with the results which is obtained by using the original survey map, the location algorithm based on GPR has higher positioning accuracy. Among them, the GPRWKNN algorithm has the highest positioning accuracy, with 80% positioning error of 132 m. The above results indicate that the method of using GPR to expand the map and further predict the location is accurate and effective, and can meet the high requirements of positioning accuracy in new application scenarios such as commodity recommendation and material dynamic management, emergency rescue, intelligent parking, infectious disease tracking and so on.