Abstract: In recent years, the quantitative measurement of individual psychology has attracted more and more attention of administrators and researchers. It has become a new trend to use Ising model for analyzing the psychological scale data. In this paper, aiming at the shortcomings of the existing Ising model, we propose a multi-class Ising model and an ordinal Ising model. By applying them to analyze a large-scale psychological scales data set, we verify the performance of the two improved Ising models, construct complex networks of psychological scales for different groups of people, and conduct the comparisons of various indicators. Some meaningful conclusions have been drawn from the constructed psychological networks, and how machine learning and big data can be better involved in the analysis of psychological scale big data is discussed as well.

Key words: psychological scale big data, learning to rank, psychological network, structural analysis