Sheet metal forming technologies, such as stamping and deep drawing, have been widely used in automotive, rail and aerospace industries for lightweight metal component manufacture. It requires specially customised presses and dies, which are very costly, particularly for low volume production of extra-large engineering panel components. In this paper, a novel recursive tool path prediction framework, impregnated with a deep learning model, is developed and instantiated for the forming sequence planning of a consecutive rubber-tool forming process. The deep learning model recursively predicts the forming parameters, namely punch location and punch stroke, for each deformation step, which yields the optimal tool path. Three series of deep learning models, namely single feature extractor, cascaded networks (including state-of-the-art deep networks) and long short-term memory (LSTM) models are implemented and trained with two datasets with different amounts of data but the same data diversity. The learning results show that the single LSTM model trained with the larger dataset has the most superior learning capability and generalisation among all models investigated. The promising results from the LSTM indicate the potential of extending the proposed recursive tool path prediction framework to the tool path planning of more complex sheet metal components. The analysis on different deep networks provides instructive references for model selection and model architecture design for sheet metal forming problems involving tool path design