本文针对现有数字虚拟资产的分类问题,设计了一种基于统计分析的数字虚拟资产分类流程模型,阐述了该模型的主要组成模块,重点介绍了每个模块所使用的关键技术方法——向量空间模型法、核主成分分析法及自组织特征映射神经网络算法,并对每种方法编写了程序实现的代码。 Aiming at the existing classification problems of digital virtual assets, this paper designs a digital virtual asset classification process model based on statistical analysis, expounds the main components of the model, and focuses on the key technical methods used by each module - vector Space model method, principal component analysis and self-organizing feature map neural network algorithm, and for each method to write the program code.