Entering the era of knowledge economy, various academic researches are blossoming under the social environment of double creation, but looking at the disputes of intellectual property cases in recent years, most of them happen in the college students group, the reasons for this phenomenon can be summarized as the blurring of the intellectual property education program, the system is too traditional and conservative, and the teaching concept is thin and so on. In view of this phenomenon, the author proposes to cultivate the intellectual property education of university students in the mode of "three constructions", so as to stimulate the vigor of social innovation and provide theoretical support for the scientific and technological research of university students.

With the continuous promotion and deepening of quality education, new teaching goals have been proposed for major universities and teachers, requiring teachers not to blindly pursue the academic performance of college students as the goal, but to achieve the comprehensive development of college students as the main teaching goal. Therefore, teachers need to actively transform educational concepts, transform educational methods, enrich classroom content, and provide high-quality teaching classrooms for college students, Help college students improve in all aspects. For college students, it is not only necessary to cultivate correct worldviews and values, establish positive life goals and attitudes, but also to enhance their resistance to pressure when facing society. Therefore, when teaching, teachers not only need to explain knowledge, but also serve as guides on the life path of college students, helping them guide and improve their ideological and moral character, Thus achieving significant development of ideological and political education in universities.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

With the advent of the era of globalized economy, more attention should be paid to the training mode of comprehensive English literacy in colleges and universities in teaching. Therefore, how to cultivate and improve students' higher-level teaching methods in the context of ecolinguistics, so as to improve the quality of English teaching, is the current focus of English teaching in colleges and universities. By summarizing the basic concepts of ecolinguistics, this paper studies effective measures to improve the quality of linguistics teaching in colleges and universities, and comprehensively improves the quality and efficiency of English language teaching in colleges and universities under the background of ecolinguistics.

In this Data science research on Education, it analyses the alcohol consumption, parent’s education, study time and other factors may influence on student performance.