In a thermoforming process in which a steel plate is formed into a curved shape with line heating, it is noteasy to select heating positions to obtain the desired shape, so it mainly depends on the empirical knowledgeof skilled workers. In this study, using Faster R-CNN, a deep learning model was proposed that takes thedesired surface as an input and the heating positions to obtain it as an output. The data sets for training themodel were obtained by using a finite element analysis model that can predict the deformation according tothe heating process parameters. The model was trained by setting color map images of various deformedshapes obtained from the analysis as input data sets and heating positions that caused deformations as outputdata sets. Using the trained model, an arbitrary deformed shape image was input, and as a virtual objectexisting in the image, the positions of the heating lines could be predicted. As a result of the test, it wasshown that the proposed model predicts the heating lines very accurately. In a validation of the proposedmodel, the performance of accurately predicting the heating positions for forming the desired shape wasshown.