Accurate temperature control and effective oxide removal are essential for achieving high−quality epitaxial growth in molecular beam epitaxy (MBE). However, traditional methods often rely on manual identification of Reflection High−Energy Electron Diffraction (RHEED) patterns. This process is heavily influenced by the grower’s experience, leading to issues with reproducibility and limiting the potential for automation. In this report, we propose an unsupervised learning framework for real−time RHEED analysis during the deoxidation process. By incorporating temporal similarity constraints into contrastive learning, our model generates smooth and interpretable feature trajectories that illustrate transitions in the deoxidation state, thus eliminating the need for manual labeling. The model, pre−trained using grouped contrastive loss, shows significant improvement in RHEED feature boundary discrimination and localization of critical regions. We evaluated its generalizability through two transfer learning strategies: calibration−free clustering and few−shot fine−tuning. The pre−trained model achieved a clustering accuracy of 88.1% for GaAs deoxidation samples without additional labels and reached an accuracy of 94.3% to 95.5% after fine−tuning with just five sample pairs across GaAs, Ge, and InAs substrates. This framework is optimized for resource−constrained edge devices, allowing for real−time, plug−and−play integration with existing MBE systems and swift adaptation across various materials and equipment. This work paves the way for greater automation and improved reproducibility in semiconductor manufacturing.