This paper proposes an end-to-end framework integrating advanced cloud computing and machine learning for NDVI time series analysis and environmental change detection. The framework achieves efficient multi-source data access, quality control, and fusion analysis through modular design, and improves the accuracy and noise robustness of change identification by combining self-attention and recursive hybrid models.