The invention discloses a method for mining an association relationship of time series data based on change consistency. The method comprises the steps of firstly preprocessing time series data variables; secondly performing wavelet transform on a single variable, dividing an original time series into a plurality of windows by using a sliding window, performing discrete wavelet transform on each window, and extracting maximum wavelet detail coefficients; thirdly performing WDC gathering on the maximum wavelet detail coefficients of all the windows of the single variable to distinguish the windows with wavelet features different from those of the most windows, wherein the windows correspond to change points of the variable; and finally performing CCP clustering on the change points of all the variables, wherein the change points of the variables in a same cluster of a clustering result are similar, so that the variables have change consistency and are regarded to have a potential association relationship. According to the method, starting from the perspective of change consistency among the variables, the variables with a linear association relationship can be discovered and the variables with a complex nonlinear association relationship can be detected, so that the method has an important effect for associative analysis among variables of a large complex system.