## 1 Introduction  
Smartphones are becoming the main way for people to receive information. Mobile applications are roughly divided into four categories: instant messaging, search engines, online news, and social applications [1]. Among them, social applications have the advantages of convenient and simple operation and have become the main way for people to communicate remotely. The interaction between users forms a virtual social structure based on the Internet platform, which is composed of the directed relationship between individuals [2]. Based on the interpersonal network relationship, social media such as WeChat and Microblog strengthen the user viscosity and play an important guiding role in the process of consumers' access to shopping information, which provides a good solution for e-commerce promotion and reduces the cost of the business. Based on the increased relationship flow and information flow of the platform, the Microblog network has the advantages of a large user base, high user activity, and user interest orientation. Users can independently pay attention to the home page of interest and express their views. The instantaneity and openness of the information platform promotes the gathering of users with common values and interests, and user communities will form gradually [3]. The current community detection methods usually divide users based on their basic attributes (age, gender, educational level, place of birth, etc.), which do not reveal the characteristics of user groups based on product information [4].  
The first step to grasp the topics of users' interest and to achieve accurate marketing is the hierarchical management of social network users. This paper takes Microblog as example to research the social network clustering structure of massive and high-dimensional user data. Text mining is the discovery of textual semantics and potentially valuable knowledge from the large-scale text set to assist people in making decisions. The large-scale short text on the mobile social platform has loud data noise and sparsity compared with the conventional text on the traditional Internet platform web page, which increases the difficulty of text analysis [3,4]. Traditional text clustering methods cannot accurately reflect the similarity of texts by calculating the distance of high-dimensional sparse feature sets because of the curse of dimensionality [5,6]. An effective method to solve high-dimensional problems in documents is to transform or select features and reduce the dimension, then cluster the refined data. This kind of method is based on the correlation hypothesis between features, which will lose the original information of the data, affect the clustering results to some extent, and the interpretability of the clustering results is difficult [7]. As a research focus of cluster analysis in recent years, sparse subspace clustering (SSC) is suitable for sparse text analysis [8,9]. Based on the semantic features of the text, this paper proposes a new online social e-commerce consumer grouping detection method based on SSC, which may solve the curse of dimensionality and sparsity in short text clustering to some extent effectively.