This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

In recent years, China has been emphasizing the importance of "mass entrepreneurship and innovation". Through such policies, more outstanding "great country craftsmen" should be cultivated, providing strong support for the overall industrial upgrading of our country. In order to achieve this grand national strategic goal, each university needs to conduct targeted exploration of the integration of innovation, entrepreneurship, and craftsmanship spirit based on its own actual situation. This article will explore the integrated cultivation mode of entrepreneurship and innovation+craftsmanship spirit from multiple aspects such as national policy guidance, student training plans, and training channels, based on the specific situation of the current development of entrepreneurship and innovation, combined with the research results of our school. In the process of entrepreneurship and innovation education, we will cultivate students' craftsmanship spirit and provide sufficient assistance for social development.

The advent of the era of big data has brought great changes to accounting work, and vocational colleges and universities, as the main place for cultivating application-oriented new business talents, need to change the way of talent training in time in the face of this change. By describing the impact of the era of big data on the demand for new business talents, this paper analyzes the analysis of the training of new business and scientific and technological talents in vocational colleges and universities in the era of big data from the perspectives of talent training target positioning, professional curriculum setting and teacher quality, accurately locates the talent training goals of new business professional groups in vocational colleges, scientifically sets up the curriculum system, and comprehensively improves the teaching staff.

As a result of China's evolving higher education landscape, private universities have emerged as significant players, fostering democratization and fulfilling key roles. However, these institutions face distinct challenges shaped by legal, societal, and internal factors. In the knowledge-driven economy, employee satisfaction is crucial for success. Understanding pivotal factors and conducting satisfaction surveys are essential for effective management and talent retention. This study focuses on Chengdu's private university educators, analyzing how factors like belongingness, self-actualization, and rewards influence job satisfaction. Through surveys, data analysis, and literature review, this study refines its findings and uncovers underlying causes. The study offers actionable insights for educators and institutions, aimed at enhancing job satisfaction.