At present, the tool remaining useful life prediction technology is important to the effectiveness of machining, because tool life prediction plays the role of safety maintenance, cost optimization and quality assurance. However, this the technology faces many challenges in practical applications. The main problems include that when the spatial distribution of data features is too different, the model is difficult to adapt to multi-scene data and the feature extraction of data time series is not obvious. Therefore, this paper proposes a method for predicting the remaining useful life of milling cutters by using multi-scale spatial data feature visualization and domain separation prediction network (MTF-SE-DSPNs). Firstly, the one-dimensional time series data are globally normalized by this method, and then the processed data are transformed into images by MTF, which enhances the time series features expression ability of data. At the same time, the convolutional neural network based on DenseNet architecture is used and SElayer is added to adjust the feature weight to mine the sensitive information in the signal. To improve the prediction ability of the model, the time decay factor ξT is introduced to optimize the reconstruction loss, so that it can dynamically measure the relative importance of source domain and target domain data and improve the robustness of feature information reconstruction. Finally, the effectiveness of the method is validated by milling experiments under the same and different working conditions. The experimental results are compared with the other models, which proves the significant advantages of the model in various tasks.