At this stage, many large domestic logistics companies have problems such as complex processes, low efficiency, and chaotic vehicle management. This usually leads to vehicle failure, material loss, or improper installation of the company’s factory, which affects the company’s interests and damages the company’s reputation. In response to the above problems, this article proposes, designs, and implements an intelligent logistics management system based on the Internet of Things technology through the research of the Internet of Things technology at home and abroad. The specific content is as follows: one is to study the origin and current situation of the Internet of Things technology at home and abroad, to understand related theories and cutting-edge technologies of the Internet of Things; the other is to imitate intelligent logistics companies, study the processes and existing problems of the logistics link, and integrate the management concepts of the Internet of Things. A model of intelligent logistics enterprise management is established with enterprise intelligent technology; the third is to conduct an experiment on the management of an intelligent logistics enterprise and do an experimental data analysis of the management of an intelligent logistics enterprise. Experimental research shows that the data of factors affecting logistics operations in smart logistics companies are low, and smart logistics companies are operating in good condition, and the established data model is suitable for smart logistics enterprise management. The Internet of Things and intelligent logistics management are both emerging concepts, while the intelligent logistics management based on the Internet of Things is still in its infancy, and relevant scholars have little research on the intelligent logistics management based on the Internet of Things. It plays a guiding role for the intelligent logistics management of enterprises and promotes the management of intelligent logistics enterprises.

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.