Raman spectroscopy can identify the spectral characteristic peaks of plastic products, but the operation process is complicated and the accuracy needs to be improved. Therefore, a classification algorithm for plastic products based on one-dimensional convolution neural network (1-D CNN) is proposed. Firstly, data sets of 40 kinds of plastic packaging samples using polyethylene, polypropylene, polyethylene terephthalate and polystyrene as raw materials were established. Then, four algorithm models including 1-D CNN, KNN, DT and SVM were designed for training, and the spectral classification process, model accuracy and robustness were compared. The experimental results show that the classification accuracy of 1-D CNN can reach 98.62% without pretreatment. And the accuracy rate is 96.42% under 60 dB noise, which is better than the three traditional machine learning algorithm models. The results show that the multi-classification method of Raman spectral fusion neural network can improve the detection performance of plastic products.