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.

A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy.

Gross domestic product (GDP) is an important indicator for determining a country's or region's economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country's or region's future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.

International Trade Path with Multi-Polarization based on Multidirectional Mutation Genetic Algorithm Enabled Neural Network.

At present, the development speed of international trade cannot catch up with the economic development speed, and the insufficient development speed of international trade will directly affect the rapid development of national economy. In order to solve the problem of international trade, the overall optimal scheduling of trade vehicles and the optimal planning of trade transportation path are very important to improve enterprise services, reduce enterprise costs, increase enterprise benefits, and enhance enterprise competitiveness. The second development of the program is based on the programming interface provided by Baidu map. This paper proposes a neural network algorithm for genetic optimization of multiple mutations, which overcomes the shortcoming of traditional genetic algorithm population "ten" character distribution by mixing multiple coding methods, and enhances the local search ability of genetic algorithm by introducing a new large-mutation small-range search population. The example application shows that the optimization method can realize the optimization of international trade path under real road conditions and greatly improve the work efficiency of actual trade.

How to Reduce Employees' Turnover Intention from the Psychological Perspective: A Mediated Moderation Model.

BACKGROUND: The hospitality industry is deemed a great generator of global GDP and employment. However, high rates of voluntary turnover have gradually undermined global service organizations and brought huge losses to them. Nowadays, the hotel sector continues to be plagued by high turnover rates. PURPOSE: A research model investigating job attitudes (job satisfaction and organizational commitment) as mediators of the impact of psychological capital (PsyCap) on turnover intention and also examining position as a moderator between job attitudes and turnover intention was proposed and tested. METHODS: This study collected data from 406 employees selected from four-star and five-star hotels in the southwest region of China. Online survey questionnaires and a purposive sampling technique were employed in this study. Structural equation modeling was utilized to evaluate the direct, mediating, and moderating effects. RESULTS: The results showed that organizational commitment and job satisfaction fully mediated the association between PsyCap and turnover intention. Moreover, position played a moderating role on the effect of the aforementioned two job attitudes on turnover intention. CONCLUSION: The findings implied that hoteliers should focus on employees' PsyCap and job attitudes in order to mitigate serious turnover issues in the hotel sector in China. Besides, the fact that position resulted in disparity impacts in the formation of turnover intention was evidenced.