To better study the non-independent and identically distributed multi-scale categorical data sets, based on the unsupervised coupling measure similarity method, a multi-scale clustering mining algorithm for non-independent and identically distributed classification attribute data sets is proposed. Firstly, the data set of benchmark scale is clustered based on coupled metric similarity method. Secondly, scale conversion algorithms upscaling based on single chain and downscaling based on Lanczos kernel are proposed for scale conversion. Finally, experiments are performed using the public data sets and the real data sets of the H province. In the experiment, couple metric similarity (CMS), inverse occurrence frequency (IOF), hamming distance (HM) and other similarity metric methods combined with spectral clustering algorithm are compared and the experimental results demonstrate that the NMI value of the upscaling increases by 13.1%, the mean of MSE value reduces by 0.827, and the mean of F-score value increases by 12.8%. Compared with other comparison algorithms, the mean of NMI value of downscaling increases by 19.2%, the mean of MSE value reduces by 0.028, and the mean of F-score value increases by 15.5%. Experimental results and theoretical analysis show that the proposed algorithm is effective and feasible.