This paper aims to explore how to build a sustainable peace and development model for China’s peacekeeping efforts through the application of data-driven methods from UN Global Pulse. UN Global Pulse is a United Nations agency dedicated to using big data and artificial intelligence technologies to address global challenges. In this paper, we will introduce the working principles of UN Global Pulse and its application in the fields of peacekeeping and development. Then, we will discuss the current situation of China’s participation in peacekeeping operations and how data-driven methods can help China play a greater role in peacekeeping tasks. Finally, we will propose a sustainable peace and development model that combines data-driven methods with the advantages of China’s peacekeeping efforts to achieve long-term peace and development goals.

Under the background of the continuous development of science and technology, the era of big data has come in an all-round way, and big data technology has also been widely used in the education industry. The course of financial management in applied colleges and universities is a highly applied course, which focuses on the substance of the course. Teachers need to create a good learning environment for students with the help of information technology, and constantly cultivate students' professional skills and professionalism. In order to improve the quality of financial management courses in colleges and universities, this paper mainly analyzes the management courses in application-oriented colleges and universities, expounds the factors affecting the practical teaching quality of management courses in colleges and universities, and analyzes the teaching methods of management courses in application-oriented colleges and universities. Finally, it is concluded that only when teachers constantly improve their teaching level, can students' learning level be improved by combining theory with practice.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.

Data mining technology is a product of the development of the new era. Unlike other similar technologies, data mining technology is mainly committed to solving various application problems, and the main means of solving problems are to use big data technology and machine learning algorithms. Simply put, data mining technology is like panning for gold in the sand, searching for useful information among massive amounts of information. Data mining technology is widely applied in various fields, such as scientific research and business, and also has its shadow in the education industry. Currently, major universities are applying data mining technology to teaching quality evaluation. This article first explains the impact of data mining technology on the education industry, and then specifically discusses the application of data mining technology in the evaluation of teaching quality in universities.

Terms and Conditions are always encountered when using social media applications to determine which data can be accessed and what cannot.  However, there are shortcomings in their implementation and communication, often causing users to be unwilling to read them. Therefore, this study aimed to analyze the effectiveness of implementing partial consent in Terms and Conditions concerning user Data Awareness and Data Security in social media. This Paper administered a questionnaire, distributed with a form, to students who use social media to understand their opinions regarding the partial consent concept. This paper analyzed the data using descriptive statistical methods. The results show a positive response from respondents towards implementing the partial consent concept, the users feel the terms and conditions are more effective in increasing user data awareness and security.