近年来，对个体的心理进行研究与量化分析越来越受到研究者们的关注，利用Ising模型对心理量表数据进行分析已经成为一种新的趋势，但是Ising模型容易造成信息丢失.为此该文对其进行了改进，提出了一种多类Ising模型和一种序Ising模型，并在一个大规模的心理量表数据集上进行了分析，验证了两种改进模型的性能.此外，针对不同人群的心理量表数据构建了相应的心理量表复杂网络，且进行了各项指标对比，从构建的心理量表复杂网络中得到一些有意义的结论.最后讨论了机器学习与大数据如何更好地介入到量表大数据中分析问题.
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.
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