Thermal error modeling and compensation is an important tool to improve the machining accuracy of machine tools. It is important to apply the obtained thermal error models to similar tasks to reduce the cost of model construction and data collection. In this article, an easy transfer learning (EasyTL) with intra-domain alignment method for spindle thermal error modeling is proposed to realize the transfer reuse of error models under different working conditions. Further, the respective effects of different types of intra-domain alignment and distance matrices on model migration performance are analyzed. Finally, the EasyTL model is compared and validated with machine learning kNN and deep learning CNN to predict the thermal errors of the Z-direction and Y-direction of spindle under different working conditions, respectively. This method provides a new idea for modeling and compensating the thermal errors of machine spindles. In addition, a workpiece compensation machining experiment is carried out according to the thermal error of the spindle established by the thermal error prediction. The average error of the workpiece after compensation is reduced. This method provides a new idea for the thermal error modeling and compensation.