For tool machinery, the most crucial factor affecting the machining precision is thermal deformation. Thus far, the most popular method of reducing thermal deformation has been considered as the compensation method, and many mathematical compensation methods have been proposed. However, attempts to develop a more comprehensive model are continuing. To improve the prediction accuracy, in this study, we propose a two-stage integrated data-mining scheme. The first stage, using rough set theory, focuses on how to manipulate the measured problem-dependent temperature and deformation data. The second stage, using a deep-learning neural network scheme, models the relationship between the temperature increase and the thermal error. Comparisons of the proposed method with other methods are also made. Results show that marked improvements are obtained using our proposed integrated data mining scheme.