Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 530-534.doi: 10.11896/JsJkx.190700124

• Database & Big Data & Data Science • Previous Articles     Next Articles

Research on Mobile Game Industry Development in China Based on Text Mining and Decision Tree Analysis

ZHU Di-chen1, XIA Huan2, YANG Xiu-zhang1, YU Xiao-min2, ZHANG Ya-cheng1 and WU Shuai1   

  1. 1 School of Information,Guizhou University of Finance and Economics,Guiyang 550025,China
    2 Guizhou Key Laboratory of Economics System Simulation,Guizhou University of Finance and Economics,Guiyang 550025,China
  • Published:2020-07-07
  • About author:ZHU Di-chen, born in 1995, master.His main research direction is data analysis library information.
    XIA Huan, doctor, professor.His main research direction is computer simulation big data analysis.
  • Supported by:
    This work was supported by the Education department of Guizhou Province Youth Science and Technology Talent Development ProJect (qianJiaoheKYzi172) and Guizhou University of Finance and Economic 2019 Annual Student Research Assistance Program (2019ZXSY54).

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Abstract: In view of the problems of inaccurate theme identification,lack of using data mining and visualization analysis method in the development of traditional mobile game industry in China,this paper proposes a research method based on text mining and decision tree analysis for the development of China’s mobile game industry.It analyzes the factors that influence the revenue and popularity of mobile game from many aspects,evaluates the characteristics of the industry from multiple perspectives,and studies the relationship between its revenue and the degree of visualization,the types of games,cultural background and internationalization index.In this paper,a detailed experiment is conducted with Python language,and the relationship between the developer and the local science and technology innovation index is analyzed,so as to dig out the mobile game with high intelligent recommendation popularity and playability.The experimental results show that the proposed algorithm has certain theoretical significance and research value,and can be applied to fields of mobile game market analysis,mobile game evaluationrecommendation and so on.Meanwhile,it can optimize the mobile game industry market in China and promote its development.

Key words: Data analysis, Decision tree analysis, Mobile game market in China, Python, Text mining

CLC Number: 

  • N37
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