Abstract: Winding hot-spot temperature is a core indicator affecting dry-type transformers'insulation life and operational safety. Existing monitoring technologies have response lag and insufficient prediction accuracy. This paper proposes an automatic prediction technology based on a three-layer architecture. It collects multi-source data via various sensors, preprocesses data with the 3σ criterion and moving average filtering, and builds a model integrated with dynamic correction of wind speed and load change rate. Validated on a 2500kV·AF prototype, the technology realizes real-time accurate prediction, resolving traditional methods'lag and