Helical milling tool wear monitoring methods based on traditional machine learning require complex feature extraction and rich experience, and different wear stages have the same misclassification cost. A new method based on the one-dimensional convolutional neural network (1D CNN) and cost-sensitive learning was introduced, aiming at the above problems and considering the characteristics of one dimension of current signals. Current signals are acquired through spindle, revolution shaft and feed shaft of robotic helical milling end-effector respectively as monitoring signals, and samples are divided using sliding window method to reduce network capacity and increase sample numbers and diversity at the same time. The cost matrix is introduced into the network loss function and the misclassification cost of severe wear stage is increased to make 1D CNN cost sensitive. The time domain current signals are input into 1D CNN directly, and the network can extract tool wear features automatically and unify feature extraction and classification of different wear stages together. Experiment results demonstrate that the tool wear monitoring accuracy of the proposed method is 99.29% and the recall of severe wear stage is 99.60% in the robotic helical milling system.