Abstract: To construct a classification model for paper-based pharmaceutical packaging materials using infrared spectroscopy technology, this paper employs infrared spectroscopy to examine 150 samples of paper-based pharmaceutical packaging materials. The samples are classified into three major categories based on the differences in infrared absorption peaks of chemical fillers. Based on the classification results, six specific wavelengths are selected, and variance normalization is applied for data preprocessing. Subsequently, a one-dimensional convolutional neural network (1D-CNN) model enhanced by residuals and attention mechanisms is developed. All samples are divided into seven categories according to the selected wavelengths and the dataset is split into training and testing sets at a ratio of 3:7. The test results indicate that the model exhibits good overall accuracy across all wavelengths, with the All-select group achieving the most notable accuracy rate of 98.10%. This demonstrates that the attention mechanism-enhanced one-dimensional convolutional neural network (ATTN-1D-CNN) model, combined with infrared spectroscopy technology, is capable of efficiently classifying and predicting paper-based pharmaceutical packaging materials.