In the process planning stage of parts, the generated process schemes strongly depend on the process knowledge selected and applied by designers. However, due to the many deviations between the actual manufacturing logics and the process knowledge selected by designers, the mismatch between the generated process scheme and the actual process has become a problem of concern in the current parts manufacturing field. This study proposes a decision method for processes of machining features driven by data and knowledge to solve the above problems. In this method, an MLP deep learning algorithm based on an attention mechanism is utilized to mine process knowledge from structured process data and correlate machining features with feature process labels. After data processing, the method is applied to train a neural network model. After verification, the method can take the feature process data of parts as input and output the distributions of corresponding feature process labels, providing decision support for the generation of the process scheme of parts.