  The application of the Internet in the field of education is becoming more
and more popular, and a large amount of educational data is generated in the
process. How to effectively use these data has always been a key issue in the
field of educational data mining. In this work, a machine learning model based
on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth
analysis of educational big data to evaluate student performance. The LSTM
model efficiently processes time series data, allowing us to capture
time-dependent and long-term trends in students' learning activities. This
approach is particularly useful for analyzing student progress, engagement, and
other behavioral patterns to support personalized education. In an experimental
analysis, we verified the effectiveness of the deep learning method in
predicting student performance by comparing the performance of different
models. Strict cross-validation techniques are used to ensure the accuracy and
generalization of experimental results.
