The purpose of this study is to develop a simple, rapid, accurate, and non-destructive classification method for black ballpoint pen ink markings. This method is designed to address the need for a reliable and efficient technique capable of distinguishing ink signatures, which is critical for applications such as document authentication, forensic investigations, and trace evidence analysis. By using advanced spectroscopic techniques, the goal is to create a practical solution that allows for precise ink classification while preserving the integrity of the samples, making it suitable for use in real-world scenarios that require fast and reliable results. A portable differential Raman spectrometer was employed to examine 102 black ballpoint pen ink samples. The spectrometer was set to dual-frequency output (Δλ ≤ 1 nm), with a single-frequency laser power of 250 mW, linewidth ≤ 0.06 nm, and a wavelength of 785 nm. The spectral range covered 180-2 800 cm^-1, and the scanning time for each sample was 3 seconds. The raw spectra were preprocessed using the Z-score standardization method, and a spectral clustering model was applied for sample classification. Additionally, a two-layer feedforward neural network was used to train the data.The 102 black ink samples were classified into six distinct categories. Further analysis showed that most samples contained titanium dioxide and titanium blue pigments. Oxalates and talcum powder exhibited weak correlations in the majority of the samples, while phenol and polystyrene showed significant correlations in some samples, though this relationship was less apparent in others. Spectral clustering further divided the samples into five categories. The accuracy of the differential Raman spectroscopy method was 97.1% for both the training and testing sets in the feedforward neural network. ROC curve analysis demonstrated high accuracy, indicating excellent performance in the classification task. This method provides a simple, fast, and non-destructive approach to classifying black ballpoint pen ink markings, offering a new tool for ink classification and identification. With its high accuracy and ease of use, this method shows great potential for applications in document authentication, forensic analysis, and other areas where ink classification is crucial. The ability to perform accurate classification without damaging the samples makes it an ideal solution for various practical uses.