文化产业的产业关联研究——基于网络交易大数据

Research of Cultural Industry Relevance——Based on Big-Data of Alibaba-Cloud-data Platform

产业关联是产业融合和产业选择的基础,本文运用阿里数据平台提供的文化产业网络交易大数据构建文化产业的复杂网络,研究了文化产业的产业关联。通过最可能最大流算法给出图谱式文化产业复杂网络形象直观地反映产业关联,同时测度文化产业和国民经济其他产业间的前向供给关联系数和后向需求关联系数,并对其进行了关联效应分解:文化产业与其他产业的关联程度迥异,文化产业对商务服务等11大行业有正向推动效应,而对农业等13大行业有负向挤出效应;最后,动态模拟显示:文化产业投入变化1%的情况下文化产业与其他产业关联关系的变化规律和对国民经济中其他产业的拉动作用,即文化产业每增加投入1个单位能带动其他产业增加的8.13个单位产出。

Industry relevance is the mainstay for the research of the industrial integration policy, Cultural industry has been run throughout in the economic and social fields present a muhidirectional interactive integration trend. Cultural industry relevance not only has the intrinsic relation between supply chain management, vertical integration strategy and strategic alliances in cultural industry companies, but also, has the intrinsic relation between upgrading of an industrial structure, industrial agglomeration and competitive advantage in regional economic development. This paper use the big-data of the manufacturers of cultural products on the Alibaba-Cloud-data platform（ The biggest B2B platform of New Economic in China）, establishes the culture industry complex network （ICN） in terms of the most reliable maximum flow arithmetic （MRMF） on uncertain graph, which shows the industry relevance of cultural industry by visualizing various data. The request of enterprise＇ s development strategy is to design the optimum route in cultural industry companies＇ industrial chain, and the policy of integration is to configure the management of industrial chain, all of these problems can be viewed as the MRMF in graph theory. Furthermore, measure the industry forward and backward relevance index between culture industries and other industries. The measurement of industry forward and backward relevance index of ICN is a complicated project related to many details, especially having excessive uncertainty and involving extensive domains. As to the identity of cultural industry, the industry relevance effect is widely different. The industry relevance effect could be decomposed into the positive effect lead to impetus and negative effect lead to crowding-out. These sectors with positive effect are listed as follows ： business services ; processing; electrician electrical ; decoration, building materials ; textiles, leather; beauty makeup cosmetic; fine chemicals; rubber and general merchandise; and sectors of agriculture; food and beverage ; underwear; hardware, tools; transportation; chemicals; environmental protection; appliances; machinery and equipment industry; energy; clothing accessories, jewelry; shoes and clothing could be negative effect when integrate with cultural industry, according to the study of industry relevance. As to find the inner structure and mutual affections between culture industry and out industries, empirical research are used to calculate the con- tribution to the whole national economy by change 1% of investment of culture industry： the total influence over 2115 hundred million Yuan, 25.7% of cultural industrial added value, which equal to 0.79% of noncultural in- dustrial added value. It shows that pre-unit input of cultural industry and drive 8. 13 unit output of national economy, at 1% transformation of cultural industry. Optimize the industry relevance is very important to enterprise management and macroeconomic regulation, and the requirement of economy works well. In consideration of positive effect of industry relevance, cultural industry preferred to integrate with frequently occurring industries in the most reliable maximum flow, like tertiary industry. The model of ICN has a certain probability properties. the scaling factor of trade share, which endow the different of Big-Data platform and algorithm optimization of Data can make sense to this probability, and this is also the This paper, has improved the MRMF by recommending economical connotations in ICN. With the development Mining , digital characteristics of treatments in real world direction of future research to deepen.