The invention discloses a principal-component analysis-based construction method of a multivariate hydrological time series matching model. Combined-model construction of multivariate hydrological time series similarity matching is carried out on the basis of principal-component analysis (PCA) and dynamic-time-warping (DTW) methods. The construction method includes: firstly, carrying out isomorphic processing on original data, wherein a Z-score standardization method is adopted; then carrying out piecewise aggregate approximation (PAA) processing on the processed data, and carrying out PCA processing on the data after PAA processing, wherein dimension reduction of the data in both a time dimension and a variable dimension is realized after the two times of processing; and finally, using aweighted DTW method for similarity matching, and obtaining a time series, which is most similar to a given time series, by matching. The construction method improves accuracy and time efficiency of similarity matching, provides services for hydrological forecasting and hydrological data analysis, and has higher application values for needs of water conservancy informatization and water conservancymodernization.