Aiming at the limitation of a single distance metric in the traditional indoor WiFi fingerprinting algorithm and the relationship between dBm representation and power is not considered, an indoor WiFi fingerprinting algorithm based on voting mechanism is proposed. After collecting the received signal strength (RSS) data, first, preprocess the RSS data. Then, based on the voting mechanism, the nearest neighbors selected by each distance metric are intersected to form common neighbors, and count each the frequency of common neighbor points. Finally, the final positioning result is obtained by probability weighting. Experimental results show that the proposed method achieves a localization accuracy of 1.63 m, and the average localization accuracy is improved by 10%, 33%, and 58%, respectively, compared with the localization accuracy of KNN, Spearman, and KTCC methods. Furthermore, the localization accuracy is improved by 12% compared to the optimal localization accuracy of 1.86 m in the MAN2 dataset.