The invention discloses a weighted naive Bayes indoor positioning method based on attribute independence, and belongs to the technical field of indoor positioning, and the method comprises the following steps: building a CSI sample set of a position point; performing CSI data preprocessing; extracting main features through a PCA algorithm; establishing an offline fingerprint database; in the online stage, using a weighted naive Bayes positioning algorithm with independent attributes; in the offline stage, through multiple times of sampling analysis, knowing that CSI amplitude values of any position obey normal distribution, and therefore the mean value and the variance of the amplitude values of all the positions serve as fingerprints to be stored. In the online stage, the variance contribution rate calculated in the principal component analysis stage is used as a weight to be applied to naive Bayes classification, and the advantages of principal component analysis are maximized. According to the method, only the mean value and the variance of the CSI amplitude values measured by each reference point for multiple times need to be selected as fingerprints, the data is processed by using the principal component analysis method, the conditional independence assumption of the naive Bayes classifier is met, and the positioning precision is improved.