Application Research of Machine Learning Algorithms in Big Data Mining and Prediction

Student information mining plays a significant role in student management, yet existing information management methods are relatively outdated. Machine learning algorithms have the capability to predict future trends based on historical data. Whether it's sales forecasting, stock price prediction, weather forecasting, or disease spread prediction, machine learning algorithms can leverage big data for training, thereby enhancing prediction accuracy and reliability. This paper proposes the use of machine learning algorithms for clustering analysis of student information. Currently, the LSTM algorithm suffers from runtime issues. Hence, we propose improvements to the LSTM algorithm. We introduce the Grey Wolf algorithm into the existing algorithm to construct one with fast convergence speed and high prediction accuracy. Applying the improved algorithm to student information clustering, experimental results demonstrate a significant improvement in the accuracy of student information identification.