This thesis utilizes Google Earth Engine and machine learning to classify land use and analyze driving factors in Dalian, China, from 2000 to 2020. It employs a Random Forest algorithm on Landsat imagery to generate land use maps for four categories: agricultural land, vegetation, water, and built-up areas. Using Principal Component Analysis and Multiple Linear Regression, it identifies the primary drivers of land use change. The research demonstrates the effectiveness of cloud computing platforms for large-scale, long-term land use monitoring and provides a scientific basis for regional land management.