Abstract
The traditional methods of analyzing consumption structure have many limitations, and data acquisition is difficult, so it is hard to scientifically verify the accuracy of algorithms. With the development of Internet economy, many scientific researchers focus on mining knowledge of consumer behavior using big data analysis technology. Because consumption decisions are influenced by not only personal characteristics but also social trends and environment, it is one-sided to analyze the impact of one single factor on the phenomenon of consumption. The authors of this paper combine the consumption structure analysis method and data processing technology using data from an e-commerce platform to extract the consumption structure of cities, compare the structural differences between different periods, and then discover consumption upgrading according to swarm intelligence. The experiments prove the efficacy of the algorithm proposed in this paper compared to other similar algorithms using several different datasets, which illustrates the algorithm’s efficacy and stable performance in consumption structure analysis.
Highlights
With the continuous expansion of consumption scale, consumers’ personalized demands are becoming increasingly obvious
From the perspective of management and economics, consumption upgrading is difficult to measure, and there is no strict boundary with which to distinguish between consumption upgrading and nonupgrading, and relevant experimental data is difficult to obtain
The research on consumption function theory is mainly focused on Persistent Income Theory (PIH) and Life Cycle Theory (LCH)
Summary
With the continuous expansion of consumption scale, consumers’ personalized demands are becoming increasingly obvious. Consumer behaviors, such as purchasing decisions, are influenced by personal characteristics and interpersonal relationships, social environments, network culture, and so on. It is difficult to find individual consumption structures at the microlevel from the perspective of consumers. The authors of this paper can fully quantify the judgment process of consumption upgrading, propose a set of evaluation criteria, and use big data with comprehensive coverage of user features for mining. This paper combines the consumption structure analysis method and data processing technology to extract collective wisdom to construct an economic map that describes urban economic hotspots. The results obtained from the multiangle and multidimensional research will be comprehensive and reasonable
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