In order to better explore the consumer psychology activities of consumers and reduce the cost control risks of enterprises, this article aims to explore the relationship between online consumer behavior and e-commerce service quality and hopes to study e-commerce by dividing online consumer groups. This article uses 5G technology to track consumer consumption habits and consumption costs. First, according to the existing literature and qualitative interviews with online consumers, it is determined that the e-commerce service quality evaluation factors are the core service quality-related system reliability, efficiency, guarantee and completion, and the quality of after-sales service. Then, based on the data obtained from the questionnaire, SPSS statistical analysis software was used to analyze the reliability of the sample variables, thereby improving the quality of the sample data. We perform descriptive statistical analysis on these measurement items. Each attribute calculates the importance of each dimension. Through the analysis of experimental cases, it can be seen that the use of 5G technology can well match consumers’ consumption habits and message needs and can evaluate the quality of e-commerce services more accurately and scientifically.

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K-means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision-making system based on Fuzzy ID3 and Min-Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min-Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk-oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value.