基于ISM和离散Hopfield神经网络的社群经济影响因素研究

Research on Influence Factors of Community Economic Based on ISM and Discrete Hopfield Neural Network

基于社群经济高速发展的时代背景下,首次将离散型Hopfield神经网络应用到社群经济影响因素问题上。首先建立社群经济因素体系,通过ISM方法得出各因素间的层次结构关系。然后利用离散型Hopfield神经网络构建自媒体价值评估模型,通过MATLAB设计一个不断提高的理想评价指标来区分同一等级中的不同样本,最后,通过算例得出价值最高的自媒体。结果表明:注重提高特色化社群活动、用户参与决策、平台共享、社群文化构建这4要素更有利于提高自媒体价值,并提出了相应的对策建议,为企业及自媒体人提供借鉴价值。

In the background of a rapid community economy development,it's first time to explore the community economic influence factor problem by discrete Hopfield neural network.The paper firstly establishes an influence factors system of Community Economy,and then establishes an interpretative structural model by ISM method to analyze the hierarchical structure relationship of influence factors. Secondly it establishes the model of we-media value evaluation model with discrete Hopfield neural network method,and designs a continuously improving ideal evaluation index to distinguish different samples of the same level by MATLAB.Finally,through the numerical example,it gets the most valuable wemedia,and the result demonstrates that laying emphasis on improving the featured community activities, users participating in the decision-making,the sharing of platform,the construction of community culture help improve the value of we-media.It puts forward the corresponding countermeasures and the research provides reference value for the enterprises and we-media.