Research on the Deep Integration Method of Local Universities, Government, and Enterprises Based on Artificial Intelligence Machine Learning Methods

In this paper, profound cooperation with local universities, governments, and enterprises via AI and ML methods is studied. This is another way by which the synergy between them is enhanced to draw toward innovation and economic development. With the help of various kinds of the ML models like Decision Tree or Logistic Regression or Support Vector Machines or Random Forests or Naive Bayes Classification or Gradient Boosting and AdaBoost, this study is targeting the study of collaboration patterns between universities and the external partners, with particular focus on the success rate and the factors that contribute to the success of such collaboration. The findings indicate that RF has the highest measure of accuracy of 99, followed by AdaBoost and Gradient Boosting, all the models taken into consideration. The paper also highlights the functions of machine learning in helping make the best decisions related to the collaboration of universities, governments, and enterprises, and therefore, indicates the usefulness of practical suggestions that will result in the efficiency of the collaboration activities. It is clear from the study that major differences arise when collaboration success and government involvement are concerned for public or private universities in China or in other foreign countries. The study also presents several enhancement strategies for the integration process, which are all based on machine learning models’ predictions.