Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

Computer security technology in E-commerce platform business model construction

The global e-commerce market is expanding rapidly as the big data era advances and the e-commerce industry thrives. This paper aims to discuss the application of computer security technology in constructing an e-commerce platform business model. The research aims to find effective security technology solutions to strengthen the security of e-commerce platforms, protect user information and rights, and enhance the sustainable development of business models. Big Data-assisted E-Commerce Business Model (BD-ECBM) construction is discussed to overcome the e-commerce platform issues and positively impact marketing strategy decision-makers by raising their level of awareness. The business decision-making process is a series of steps that help businesses overcome obstacles by collecting relevant data, analyzing all relevant alternatives, and settling on a course of action. Big data analytics can improve business management with data fusion technology in many ways. The system's framework of the information service layer, the application-level layer, and the client session layer was developed using the Business model paradigm for accounting for e-past, commerce's capabilities of the platform, and an analysis of supply and demand. It can monitor its rivals in near real-time, adjusting prices, offering more attractive deals, and analyzing negative customer comments to see if it can outperform its rivals. The trial results demonstrate the effectiveness of this method of making marketing decisions and formulating a strategy. Hence, the BD-ECBM technique improves customers' overall satisfaction and experience with the brand while raising the latter's profile.

Multi-angle perception and convolutional neural network for service quality evaluation of cross-border e-commerce logistics enterprise

The development of cross-border e-commerce logistics services has injected new vitality into the development of international trade, and therefore has become a new hot spot in theoretical research. In order to ensure the healthy development of cross-border e-commerce, it is urgent to build a set of scientific and effective evaluation mechanisms to scientifically evaluate the logistics service quality of cross-border e-commerce. Multi-angle perceptual convolutional neural network is a framework for service scene identification of cross-border e-commerce logistics enterprises based on deep convolutional neural network and multi-angle perceptual width learning. In this article, both shallow features and deep features were input into the deep perception model (DPM) to obtain a set of distinguishable features with causal structure, which was used to completely describe the high-level semantic information of cross-border e-commerce logistics enterprise services. Among them, DPM mainly adopts the fusion strategy of shallow feature and deep feature. Meanwhile, the feature representation is input into the width learning pattern recognition system for training and classification, so as to evaluate the service quality of cross-border e-commerce logistics enterprises. The multi-angle perceptual convolutional neural network can effectively solve the problems of high similarity between service classes of cross-border e-commerce logistics enterprises and large differences within the class, and achieve better generalization performance and algorithm complexity than support vector machine, random forest and convolutional neural network.

Big Data Assisted Empirical Study for Business Value Identification Using Smart Technologies

The main problem for the big data for an e-commerce site is getting a meaningful data analysis, which the big descriptive statistics consider the most crucial usage. The collection, segmentation, and analysis of customer insights are critical to developing an effective and precise tailored experience for each consumer. Analyzing and segmentation of customer insights are essential to creating an effective and personalized experience for each customer. Using price optimization (BDA-PO), big data analytics has been proposed, enabling enterprising services like tourism, shopping, transportation, and creative industries to provide variable rates for products and services using Smart Technologies for E-Business and Commerce. Price optimizing can be automated with machine learning algorithms to enhance profitability when pricing decisions are taken effectively. When pricing decisions are made correctly, it is possible to automate price optimization using machine learning algorithms.

Innovative Mode of Logistics Management of "Internet of Things + Blockchain"-Integrated E-Commerce Platform

The innovation of logistics management mode plays a great role in promoting the operation timeliness of an e-commerce platform. The question of how to realize the research on the innovation mode of logistics management of the e-commerce platform with the integration of "Internet of things + blockchain" is the current development trend. Based on this, this paper studies the influencing factors of logistics management of an e-commerce platform under the discrete analysis strategy of "Internet of things + blockchain." First, the logistics management analysis model of an e-commerce platform integrating "Internet of things + blockchain" is proposed. The dynamic correlation function is used to simulate the logistics information in the e-commerce platform. Through the extreme value of the dynamic correlation function curve in the detection process, the operation signal is restored, and then its logistics management is analyzed. Second, the influencing factors in the logistics management of the e-commerce platform are analyzed, comprehensive analysis is done by using the "Internet of things + blockchain" integration model, the dynamic logistics information in the operation of the e-commerce platform is accurately gathered, and the multidimensional hierarchical method is used for quality evaluation. Finally, the effectiveness of the logistics management analysis model of the e-commerce platform is verified by many experiments.

E-Commerce Information System Management Based on Data Mining and Neural Network Algorithms

The rapid development of artificial intelligence technology has led to rapid development in various fields. It has many hidden related customer behavior information and future development trends in the e-commerce information system. The data mining technology can dig out useful information and promote the development of e-commerce. This research analyzes the significance and advantages of data mining technology in the application of e-commerce management systems and analyzes the related technologies of data mining and future trend prediction. This research has taken the advantages of clustering and naive Bayesian methods in data mining to classify product information and purchase preferences and other information and mine the associated data. Then, the nonlinear data processing advantages of neural networks are used to predict future purchasing power. The results show that data mining technology and neural networks have high accuracy in predicting future consumer purchasing power information. The correlation coefficient between real consumption data and predicted consumption data reached 0.9785, and the maximum relative average error was only 2.32%. It fully shows that data mining technology can obtain some unrecognizable related information and future consumption trends in e-commerce systems, and neural networks can also predict future consumption power and consumption patterns well.

Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm

In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%.

How Do Manufacturing Enterprises Construct E-Commerce Platforms for Sustainable Development? A Case Study of Resource Orchestration

The existing literatures mainly focus on the pricing, strategic significance and sustainable development characteristics of the e-commerce platform, and lack deep research on mechanisms in the process of construction like main structure of recourses and driving force. This paper takes Haier as a Chinese example and explores how manufacturing enterprises create and develop the sustainable e-commerce platform. The research findings show that: (1) An e-commerce platform respectively carries the functions of sales channels, service differences and innovation incubation in different stages of the manufacturing enterprises’ sustainable development; (2) For managing e-commerce platform of manufacturing enterprises’ sustainable development, resource orchestration can effectively realize the integration of value creation and resource; (3) Finally, it further reveals that the driving power which resource orchestration continuously promotes for the sustainable e-commerce platforms to construct is from the co-creation value of manufacturers and users. This paper discusses the structure of e-commerce platforms based on the main characteristics of each resource, and systematically explores the mechanism and evolutionary driving force of resource orchestration to promote the construction of e-commerce platforms for the sustainable development. It complements and enriches the innovation ecosystem and resource orchestration theory, providing significant practical guidance to the sustainable development of manufacturing enterprises.