With the rapid development of online finance and social networks, a large amount of behavioral data is stored on the Internet, which can fully reflect the shopping tendencies and habits of real users. Using big data to analyze consumer behavior is more scientific and accurate than the traditional sampling survey method. Internet consumption behavior data are time series data. Therefore, this paper proposes a method of analyzing behavioral sequence data, which learns personal consumption interests and habits, and finally predicts payment behavior. The experiments compare the execution effect of different algorithms on multiple databases and verify the feasibility and effectiveness of the proposed algorithm SeqLearn.

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.

State prediction of a node is made through analyses of adjacent nodes to learn about movements and to manage users in inference network. This study first simplifies ego network to reduce the complexity of structural analyses based on three different relationships among users. Second, degrees of mutual influences are computed according to different forms of association. Finally, the action of the centre node is predicted by analysing the changing of the states of adjacent nodes. The experiment proves that accuracy and efficiency of the algorithm proposed performs well not only in the simplification of ego network but also in the prediction of movements.

The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.

Users’ online activities serve as a mirror, reflecting their unique personas, affiliations, interests, and hobbies within the real world. Network information dissemination is inherently targeted, as users actively seek information to facilitate precise and swift communication. Delving into the nuances of information propagation on the Internet holds immense potential for facilitating commercial endeavors such as targeted advertising, personalized product recommendations, and insightful consumer behavior analyses. Recognizing that the intensity of information transmission diminishes with the proliferation of competing messages, increased transmission distances, and the passage of time, this paper draws inspiration from the concept of heat attenuation to formulate an innovative information propagation model. This model simulates the “heat index” of each node in the transmission process, thereby capturing the dynamic nature of information flow. Extensive experiments, bolstered by comparative analyses of multiple datasets and relevant algorithms, validate the correctness, feasibility, and efficiency of our proposed algorithm. Notably, our approach demonstrates remarkable accuracy and stability, underscoring its potential for real‐world applications.