【Objective】This article focuses on the digitization of historical information and data analysis of private gardens. Two new research paradigms were explored and compared with the traditional processes in terms of sample coverage and information complexity reduction. 【Method】 Forty-seven private gardens in the Xinghua Village plot in Nanjing during the Ming and Qing dynasties were examined. First, 45 gardens and 57 variables were transformed into a total of 2 565 items in a historical information matrix based on the detailed historical scrutiny. Subsequently, the information matrix was subjected to K-means clustering and the principal component analysis (PCA). Two properties, the sample coverage and information complexity reduction, were studied and compared with those of the traditional research, such as four elements, host identity, and a high-frequency feature analysis. Finally, the differences in historical laws after the above analysis were compared. 【Result】 The traditional, high-frequency analysis had a sample coverage of approximately 45% and retained 70% of the original information complexity; for the K-means clustering algorithm with a specified number of 5, the sample coverage was 42%, and 63% of the original information complexity was retained. In contrast, PCA achieved more than 70% sample coverage and only 44% of the original information complexity. Of the historical laws obtained, the traditional method could procure discrete independent information. However, both K-means clustering and PCA yielded few intersectional laws. In particular, the PCA rules showed strong suggestions and characteristics as obvious advantages. 【Conclusion】The PCA results showed that, in the context of the historical garden evolution in the Xinghua Village plot, garden elements and styles were more affected by the existing garden morphology, rather than the identity of the owners or fashions. This supersedes the common results in the existing paradigm. In conclusion, in the research of large number of private historical gardens, PCA offers the advantages of high sample coverage and low information complexity and can be considered as a standard method.